Mirella Lapata


2021

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Factorising Meaning and Form for Intent-Preserving Paraphrasing
Tom Hosking | Mirella Lapata
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We propose a method for generating paraphrases of English questions that retain the original intent but use a different surface form. Our model combines a careful choice of training objective with a principled information bottleneck, to induce a latent encoding space that disentangles meaning and form. We train an encoder-decoder model to reconstruct a question from a paraphrase with the same meaning and an exemplar with the same surface form, leading to separated encoding spaces. We use a Vector-Quantized Variational Autoencoder to represent the surface form as a set of discrete latent variables, allowing us to use a classifier to select a different surface form at test time. Crucially, our method does not require access to an external source of target exemplars. Extensive experiments and a human evaluation show that we are able to generate paraphrases with a better tradeoff between semantic preservation and syntactic novelty compared to previous methods.

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Generating Query Focused Summaries from Query-Free Resources
Yumo Xu | Mirella Lapata
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

The availability of large-scale datasets has driven the development of neural models that create generic summaries from single or multiple documents. In this work we consider query focused summarization (QFS), a task for which training data in the form of queries, documents, and summaries is not readily available. We propose to decompose QFS into (1) query modeling (i.e., finding supportive evidence within a set of documents for a query) and (2) conditional language modeling (i.e., summary generation). We introduce MaRGE, a Masked ROUGE Regression framework for evidence estimation and ranking which relies on a unified representation for summaries and queries, so that summaries in generic data can be converted into proxy queries for learning a query model. Experiments across QFS benchmarks and query types show that our model achieves state-of-the-art performance despite learning from weak supervision.

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Meta-Learning for Domain Generalization in Semantic Parsing
Bailin Wang | Mirella Lapata | Ivan Titov
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The importance of building semantic parsers which can be applied to new domains and generate programs unseen at training has long been acknowledged, and datasets testing out-of-domain performance are becoming increasingly available. However, little or no attention has been devoted to learning algorithms or objectives which promote domain generalization, with virtually all existing approaches relying on standard supervised learning. In this work, we use a meta-learning framework which targets zero-shot domain generalization for semantic parsing. We apply a model-agnostic training algorithm that simulates zero-shot parsing by constructing virtual train and test sets from disjoint domains. The learning objective capitalizes on the intuition that gradient steps that improve source-domain performance should also improve target-domain performance, thus encouraging a parser to generalize to unseen target domains. Experimental results on the (English) Spider and Chinese Spider datasets show that the meta-learning objective significantly boosts the performance of a baseline parser.

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Text Generation from Discourse Representation Structures
Jiangming Liu | Shay B. Cohen | Mirella Lapata
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We propose neural models to generate text from formal meaning representations based on Discourse Representation Structures (DRSs). DRSs are document-level representations which encode rich semantic detail pertaining to rhetorical relations, presupposition, and co-reference within and across sentences. We formalize the task of neural DRS-to-text generation and provide modeling solutions for the problems of condition ordering and variable naming which render generation from DRSs non-trivial. Our generator relies on a novel sibling treeLSTM model which is able to accurately represent DRS structures and is more generally suited to trees with wide branches. We achieve competitive performance (59.48 BLEU) on the GMB benchmark against several strong baselines.

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Noisy Self-Knowledge Distillation for Text Summarization
Yang Liu | Sheng Shen | Mirella Lapata
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

In this paper we apply self-knowledge distillation to text summarization which we argue can alleviate problems with maximum-likelihood training on single reference and noisy datasets. Instead of relying on one-hot annotation labels, our student summarization model is trained with guidance from a teacher which generates smoothed labels to help regularize training. Furthermore, to better model uncertainty during training, we introduce multiple noise signals for both teacher and student models. We demonstrate experimentally on three benchmarks that our framework boosts the performance of both pretrained and non-pretrained summarizers achieving state-of-the-art results.

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Learning from Executions for Semantic Parsing
Bailin Wang | Mirella Lapata | Ivan Titov
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Semantic parsing aims at translating natural language (NL) utterances onto machine-interpretable programs, which can be executed against a real-world environment. The expensive annotation of utterance-program pairs has long been acknowledged as a major bottleneck for the deployment of contemporary neural models to real-life applications. In this work, we focus on the task of semi-supervised learning where a limited amount of annotated data is available together with many unlabeled NL utterances. Based on the observation that programs which correspond to NL utterances should always be executable, we propose to encourage a parser to generate executable programs for unlabeled utterances. Due to the large search space of executable programs, conventional methods that use beam-search for approximation, such as self-training and top-k marginal likelihood training, do not perform as well. Instead, we propose a set of new training objectives that are derived by approaching the problem of learning from executions from the posterior regularization perspective. Our new objectives outperform conventional methods on Overnight and GeoQuery, bridging the gap between semi-supervised and supervised learning.

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Informative and Controllable Opinion Summarization
Reinald Kim Amplayo | Mirella Lapata
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Opinion summarization is the task of automatically generating summaries for a set of reviews about a specific target (e.g., a movie or a product). Since the number of reviews for each target can be prohibitively large, neural network-based methods follow a two-stage approach where an extractive step first pre-selects a subset of salient opinions and an abstractive step creates the summary while conditioning on the extracted subset. However, the extractive model leads to loss of information which may be useful depending on user needs. In this paper we propose a summarization framework that eliminates the need to rely only on pre-selected content and waste possibly useful information, especially when customizing summaries. The framework enables the use of all input reviews by first condensing them into multiple dense vectors which serve as input to an abstractive model. We showcase an effective instantiation of our framework which produces more informative summaries and also allows to take user preferences into account using our zero-shot customization technique. Experimental results demonstrate that our model improves the state of the art on the Rotten Tomatoes dataset and generates customized summaries effectively.

2020

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Screenplay Summarization Using Latent Narrative Structure
Pinelopi Papalampidi | Frank Keller | Lea Frermann | Mirella Lapata
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Most general-purpose extractive summarization models are trained on news articles, which are short and present all important information upfront. As a result, such models are biased on position and often perform a smart selection of sentences from the beginning of the document. When summarizing long narratives, which have complex structure and present information piecemeal, simple position heuristics are not sufficient. In this paper, we propose to explicitly incorporate the underlying structure of narratives into general unsupervised and supervised extractive summarization models. We formalize narrative structure in terms of key narrative events (turning points) and treat it as latent in order to summarize screenplays (i.e., extract an optimal sequence of scenes). Experimental results on the CSI corpus of TV screenplays, which we augment with scene-level summarization labels, show that latent turning points correlate with important aspects of a CSI episode and improve summarization performance over general extractive algorithms leading to more complete and diverse summaries.

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Unsupervised Opinion Summarization with Noising and Denoising
Reinald Kim Amplayo | Mirella Lapata
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The supervised training of high-capacity models on large datasets containing hundreds of thousands of document-summary pairs is critical to the recent success of deep learning techniques for abstractive summarization. Unfortunately, in most domains (other than news) such training data is not available and cannot be easily sourced. In this paper we enable the use of supervised learning for the setting where there are only documents available (e.g., product or business reviews) without ground truth summaries. We create a synthetic dataset from a corpus of user reviews by sampling a review, pretending it is a summary, and generating noisy versions thereof which we treat as pseudo-review input. We introduce several linguistically motivated noise generation functions and a summarization model which learns to denoise the input and generate the original review. At test time, the model accepts genuine reviews and generates a summary containing salient opinions, treating those that do not reach consensus as noise. Extensive automatic and human evaluation shows that our model brings substantial improvements over both abstractive and extractive baselines.

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Dscorer: A Fast Evaluation Metric for Discourse Representation Structure Parsing
Jiangming Liu | Shay B. Cohen | Mirella Lapata
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Discourse representation structures (DRSs) are scoped semantic representations for texts of arbitrary length. Evaluating the accuracy of predicted DRSs plays a key role in developing semantic parsers and improving their performance. DRSs are typically visualized as boxes which are not straightforward to process automatically. Counter transforms DRSs to clauses and measures clause overlap by searching for variable mappings between two DRSs. However, this metric is computationally costly (with respect to memory and CPU time) and does not scale with longer texts. We introduce Dscorer, an efficient new metric which converts box-style DRSs to graphs and then measures the overlap of n-grams. Experiments show that Dscorer computes accuracy scores that are correlated with Counter at a fraction of the time.

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Unsupervised Opinion Summarization as Copycat-Review Generation
Arthur Bražinskas | Mirella Lapata | Ivan Titov
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Opinion summarization is the task of automatically creating summaries that reflect subjective information expressed in multiple documents, such as product reviews. While the majority of previous work has focused on the extractive setting, i.e., selecting fragments from input reviews to produce a summary, we let the model generate novel sentences and hence produce abstractive summaries. Recent progress in summarization has seen the development of supervised models which rely on large quantities of document-summary pairs. Since such training data is expensive to acquire, we instead consider the unsupervised setting, in other words, we do not use any summaries in training. We define a generative model for a review collection which capitalizes on the intuition that when generating a new review given a set of other reviews of a product, we should be able to control the “amount of novelty” going into the new review or, equivalently, vary the extent to which it deviates from the input. At test time, when generating summaries, we force the novelty to be minimal, and produce a text reflecting consensus opinions. We capture this intuition by defining a hierarchical variational autoencoder model. Both individual reviews and the products they correspond to are associated with stochastic latent codes, and the review generator (“decoder”) has direct access to the text of input reviews through the pointer-generator mechanism. Experiments on Amazon and Yelp datasets, show that setting at test time the review’s latent code to its mean, allows the model to produce fluent and coherent summaries reflecting common opinions.

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Multi-Step Inference for Reasoning Over Paragraphs
Jiangming Liu | Matt Gardner | Shay B. Cohen | Mirella Lapata
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Complex reasoning over text requires understanding and chaining together free-form predicates and logical connectives. Prior work has largely tried to do this either symbolically or with black-box transformers. We present a middle ground between these two extremes: a compositional model reminiscent of neural module networks that can perform chained logical reasoning. This model first finds relevant sentences in the context and then chains them together using neural modules. Our model gives significant performance improvements (up to 29% relative error reduction when combined with a reranker) on ROPES, a recently-introduced complex reasoning dataset.

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Coarse-to-Fine Query Focused Multi-Document Summarization
Yumo Xu | Mirella Lapata
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We consider the problem of better modeling query-cluster interactions to facilitate query focused multi-document summarization. Due to the lack of training data, existing work relies heavily on retrieval-style methods for assembling query relevant summaries. We propose a coarse-to-fine modeling framework which employs progressively more accurate modules for estimating whether text segments are relevant, likely to contain an answer, and central. The modules can be independently developed and leverage training data if available. We present an instantiation of this framework with a trained evidence estimator which relies on distant supervision from question answering (where various resources exist) to identify segments which are likely to answer the query and should be included in the summary. Our framework is robust across domains and query types (i.e., long vs short) and outperforms strong comparison systems on benchmark datasets.

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Alignment-free Cross-lingual Semantic Role Labeling
Rui Cai | Mirella Lapata
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Cross-lingual semantic role labeling (SRL) aims at leveraging resources in a source language to minimize the effort required to construct annotations or models for a new target language. Recent approaches rely on word alignments, machine translation engines, or preprocessing tools such as parsers or taggers. We propose a cross-lingual SRL model which only requires annotations in a source language and access to raw text in the form of a parallel corpus. The backbone of our model is an LSTM-based semantic role labeler jointly trained with a semantic role compressor and multilingual word embeddings. The compressor collects useful information from the output of the semantic role labeler, filtering noisy and conflicting evidence. It lives in a multilingual embedding space and provides direct supervision for predicting semantic roles in the target language. Results on the Universal Proposition Bank and manually annotated datasets show that our method is highly effective, even against systems utilizing supervised features.

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Few-Shot Learning for Opinion Summarization
Arthur Bražinskas | Mirella Lapata | Ivan Titov
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Opinion summarization is the automatic creation of text reflecting subjective information expressed in multiple documents, such as user reviews of a product. The task is practically important and has attracted a lot of attention. However, due to the high cost of summary production, datasets large enough for training supervised models are lacking. Instead, the task has been traditionally approached with extractive methods that learn to select text fragments in an unsupervised or weakly-supervised way. Recently, it has been shown that abstractive summaries, potentially more fluent and better at reflecting conflicting information, can also be produced in an unsupervised fashion. However, these models, not being exposed to actual summaries, fail to capture their essential properties. In this work, we show that even a handful of summaries is sufficient to bootstrap generation of the summary text with all expected properties, such as writing style, informativeness, fluency, and sentiment preservation. We start by training a conditional Transformer language model to generate a new product review given other available reviews of the product. The model is also conditioned on review properties that are directly related to summaries; the properties are derived from reviews with no manual effort. In the second stage, we fine-tune a plug-in module that learns to predict property values on a handful of summaries. This lets us switch the generator to the summarization mode. We show on Amazon and Yelp datasets that our approach substantially outperforms previous extractive and abstractive methods in automatic and human evaluation.

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Zero-Shot Crosslingual Sentence Simplification
Jonathan Mallinson | Rico Sennrich | Mirella Lapata
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Sentence simplification aims to make sentences easier to read and understand. Recent approaches have shown promising results with encoder-decoder models trained on large amounts of parallel data which often only exists in English. We propose a zero-shot modeling framework which transfers simplification knowledge from English to another language (for which no parallel simplification corpus exists) while generalizing across languages and tasks. A shared transformer encoder constructs language-agnostic representations, with a combination of task-specific encoder layers added on top (e.g., for translation and simplification). Empirical results using both human and automatic metrics show that our approach produces better simplifications than unsupervised and pivot-based methods.

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Multi-view Story Characterization from Movie Plot Synopses and Reviews
Sudipta Kar | Gustavo Aguilar | Mirella Lapata | Thamar Solorio
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

This paper considers the problem of characterizing stories by inferring properties such as theme and style using written synopses and reviews of movies. We experiment with a multi-label dataset of movie synopses and a tagset representing various attributes of stories (e.g., genre, type of events). Our proposed multi-view model encodes the synopses and reviews using hierarchical attention and shows improvement over methods that only use synopses. Finally, we demonstrate how we can take advantage of such a model to extract a complementary set of story-attributes from reviews without direct supervision. We have made our dataset and source code publicly available at https://ritual.uh.edu/multiview-tag-2020.

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Experience Grounds Language
Yonatan Bisk | Ari Holtzman | Jesse Thomason | Jacob Andreas | Yoshua Bengio | Joyce Chai | Mirella Lapata | Angeliki Lazaridou | Jonathan May | Aleksandr Nisnevich | Nicolas Pinto | Joseph Turian
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world. It is this shared experience that makes utterances meaningful. Natural language processing is a diverse field, and progress throughout its development has come from new representational theories, modeling techniques, data collection paradigms, and tasks. We posit that the present success of representation learning approaches trained on large, text-only corpora requires the parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.

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Bootstrapping a Crosslingual Semantic Parser
Tom Sherborne | Yumo Xu | Mirella Lapata
Findings of the Association for Computational Linguistics: EMNLP 2020

Recent progress in semantic parsing scarcely considers languages other than English but professional translation can be prohibitively expensive. We adapt a semantic parser trained on a single language, such as English, to new languages and multiple domains with minimal annotation. We query if machine translation is an adequate substitute for training data, and extend this to investigate bootstrapping using joint training with English, paraphrasing, and multilingual pre-trained models. We develop a Transformer-based parser combining paraphrases by ensembling attention over multiple encoders and present new versions of ATIS and Overnight in German and Chinese for evaluation. Experimental results indicate that MT can approximate training data in a new language for accurate parsing when augmented with paraphrasing through multiple MT engines. Considering when MT is inadequate, we also find that using our approach achieves parsing accuracy within 2% of complete translation using only 50% of training data.

2019

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Semi-Supervised Semantic Role Labeling with Cross-View Training
Rui Cai | Mirella Lapata
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

The successful application of neural networks to a variety of NLP tasks has provided strong impetus to develop end-to-end models for semantic role labeling which forego the need for extensive feature engineering. Recent approaches rely on high-quality annotations which are costly to obtain, and mostly unavailable in low resource scenarios (e.g., rare languages or domains). Our work aims to reduce the annotation effort involved via semi-supervised learning. We propose an end-to-end SRL model and demonstrate it can effectively leverage unlabeled data under the cross-view training modeling paradigm. Our LSTM-based semantic role labeler is jointly trained with a sentence learner, which performs POS tagging, dependency parsing, and predicate identification which we argue are critical to learning directly from unlabeled data without recourse to external pre-processing tools. Experimental results on the CoNLL-2009 benchmark dataset show that our model outperforms the state of the art in English, and consistently improves performance in other languages, including Chinese, German, and Spanish.

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Movie Plot Analysis via Turning Point Identification
Pinelopi Papalampidi | Frank Keller | Mirella Lapata
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

According to screenwriting theory, turning points (e.g., change of plans, major setback, climax) are crucial narrative moments within a screenplay: they define the plot structure, determine its progression and segment the screenplay into thematic units (e.g., setup, complications, aftermath). We propose the task of turning point identification in movies as a means of analyzing their narrative structure. We argue that turning points and the segmentation they provide can facilitate processing long, complex narratives, such as screenplays, for summarization and question answering. We introduce a dataset consisting of screenplays and plot synopses annotated with turning points and present an end-to-end neural network model that identifies turning points in plot synopses and projects them onto scenes in screenplays. Our model outperforms strong baselines based on state-of-the-art sentence representations and the expected position of turning points.

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Partners in Crime: Multi-view Sequential Inference for Movie Understanding
Nikos Papasarantopoulos | Lea Frermann | Mirella Lapata | Shay B. Cohen
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Multi-view learning algorithms are powerful representation learning tools, often exploited in the context of multimodal problems. However, for problems requiring inference at the token-level of a sequence (that is, a separate prediction must be made for every time step), it is often the case that single-view systems are used, or that more than one views are fused in a simple manner. We describe an incremental neural architecture paired with a novel training objective for incremental inference. The network operates on multi-view data. We demonstrate the effectiveness of our approach on the problem of predicting perpetrators in crime drama series, for which our model significantly outperforms previous work and strong baselines. Moreover, we introduce two tasks, crime case and speaker type tagging, that contribute to movie understanding and demonstrate the effectiveness of our model on them.

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Semantic graph parsing with recurrent neural network DAG grammars
Federico Fancellu | Sorcha Gilroy | Adam Lopez | Mirella Lapata
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Semantic parses are directed acyclic graphs (DAGs), so semantic parsing should be modeled as graph prediction. But predicting graphs presents difficult technical challenges, so it is simpler and more common to predict the *linearized* graphs found in semantic parsing datasets using well-understood sequence models. The cost of this simplicity is that the predicted strings may not be well-formed graphs. We present recurrent neural network DAG grammars, a graph-aware sequence model that generates only well-formed graphs while sidestepping many difficulties in graph prediction. We test our model on the Parallel Meaning Bank—a multilingual semantic graphbank. Our approach yields competitive results in English and establishes the first results for German, Italian and Dutch.

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Text Summarization with Pretrained Encoders
Yang Liu | Mirella Lapata
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Bidirectional Encoder Representations from Transformers (BERT) represents the latest incarnation of pretrained language models which have recently advanced a wide range of natural language processing tasks. In this paper, we showcase how BERT can be usefully applied in text summarization and propose a general framework for both extractive and abstractive models. We introduce a novel document-level encoder based on BERT which is able to express the semantics of a document and obtain representations for its sentences. Our extractive model is built on top of this encoder by stacking several inter-sentence Transformer layers. For abstractive summarization, we propose a new fine-tuning schedule which adopts different optimizers for the encoder and the decoder as a means of alleviating the mismatch between the two (the former is pretrained while the latter is not). We also demonstrate that a two-staged fine-tuning approach can further boost the quality of the generated summaries. Experiments on three datasets show that our model achieves state-of-the-art results across the board in both extractive and abstractive settings.

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Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs
Bailin Wang | Ivan Titov | Mirella Lapata
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Semantic parsing aims to map natural language utterances onto machine interpretable meaning representations, aka programs whose execution against a real-world environment produces a denotation. Weakly-supervised semantic parsers are trained on utterance-denotation pairs treating programs as latent. The task is challenging due to the large search space and spuriousness of programs which may execute to the correct answer but do not generalize to unseen examples. Our goal is to instill an inductive bias in the parser to help it distinguish between spurious and correct programs. We capitalize on the intuition that correct programs would likely respect certain structural constraints were they to be aligned to the question (e.g., program fragments are unlikely to align to overlapping text spans) and propose to model alignments as structured latent variables. In order to make the latent-alignment framework tractable, we decompose the parsing task into (1) predicting a partial “abstract program” and (2) refining it while modeling structured alignments with differential dynamic programming. We obtain state-of-the-art performance on the WikiTableQuestions and WikiSQL datasets. When compared to a standard attention baseline, we observe that the proposed structured-alignment mechanism is highly beneficial.

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University of Edinburgh’s submission to the Document-level Generation and Translation Shared Task
Ratish Puduppully | Jonathan Mallinson | Mirella Lapata
Proceedings of the 3rd Workshop on Neural Generation and Translation

The University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG with both English and German as targeted languages. For the NLG track, we submitted a multilingual system based on the Content Selection and Planning model of Puduppully et al (2019). For the MT track, we submitted Transformer-based Neural Machine Translation models, where out-of-domain parallel data was augmented with in-domain data extracted from monolingual corpora. Our MT+NLG systems disregard the structured input data and instead rely exclusively on the source summaries.

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Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model
Jiangming Liu | Shay B. Cohen | Mirella Lapata
Proceedings of the IWCS Shared Task on Semantic Parsing

We describe the systems we developed for Discourse Representation Structure (DRS) parsing as part of the IWCS-2019 Shared Task of DRS Parsing.1 Our systems are based on sequence-to-sequence modeling. To implement our model, we use the open-source neural machine translation system implemented in PyTorch, OpenNMT-py. We experimented with a variety of encoder-decoder models based on recurrent neural networks and the Transformer model. We conduct experiments on the standard benchmark of the Parallel Meaning Bank (PMB 2.2). Our best system achieves a score of 84.8% F1 in the DRS parsing shared task.

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Syntax-aware Semantic Role Labeling without Parsing
Rui Cai | Mirella Lapata
Transactions of the Association for Computational Linguistics, Volume 7

In this paper we focus on learning dependency aware representations for semantic role labeling without recourse to an external parser. The backbone of our model is an LSTM-based semantic role labeler jointly trained with two auxiliary tasks: predicting the dependency label of a word and whether there exists an arc linking it to the predicate. The auxiliary tasks provide syntactic information that is specific to semantic role labeling and are learned from training data (dependency annotations) without relying on existing dependency parsers, which can be noisy (e.g., on out-of-domain data or infrequent constructions). Experimental results on the CoNLL-2009 benchmark dataset show that our model outperforms the state of the art in English, and consistently improves performance in other languages, including Chinese, German, and Spanish.

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Weakly Supervised Domain Detection
Yumo Xu | Mirella Lapata
Transactions of the Association for Computational Linguistics, Volume 7

In this paper we introduce domain detection as a new natural language processing task. We argue that the ability to detect textual segments that are domain-heavy (i.e., sentences or phrases that are representative of and provide evidence for a given domain) could enhance the robustness and portability of various text classification applications. We propose an encoder-detector framework for domain detection and bootstrap classifiers with multiple instance learning. The model is hierarchically organized and suited to multilabel classification. We demonstrate that despite learning with minimal supervision, our model can be applied to text spans of different granularities, languages, and genres. We also showcase the potential of domain detection for text summarization.

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Data-to-text Generation with Entity Modeling
Ratish Puduppully | Li Dong | Mirella Lapata
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Recent approaches to data-to-text generation have shown great promise thanks to the use of large-scale datasets and the application of neural network architectures which are trained end-to-end. These models rely on representation learning to select content appropriately, structure it coherently, and verbalize it grammatically, treating entities as nothing more than vocabulary tokens. In this work we propose an entity-centric neural architecture for data-to-text generation. Our model creates entity-specific representations which are dynamically updated. Text is generated conditioned on the data input and entity memory representations using hierarchical attention at each time step. We present experiments on the RotoWire benchmark and a (five times larger) new dataset on the baseball domain which we create. Our results show that the proposed model outperforms competitive baselines in automatic and human evaluation.

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Hierarchical Transformers for Multi-Document Summarization
Yang Liu | Mirella Lapata
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In this paper, we develop a neural summarization model which can effectively process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner. We represent cross-document relationships via an attention mechanism which allows to share information as opposed to simply concatenating text spans and processing them as a flat sequence. Our model learns latent dependencies among textual units, but can also take advantage of explicit graph representations focusing on similarity or discourse relations. Empirical results on the WikiSum dataset demonstrate that the proposed architecture brings substantial improvements over several strong baselines.

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Generating Summaries with Topic Templates and Structured Convolutional Decoders
Laura Perez-Beltrachini | Yang Liu | Mirella Lapata
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Existing neural generation approaches create multi-sentence text as a single sequence. In this paper we propose a structured convolutional decoder that is guided by the content structure of target summaries. We compare our model with existing sequential decoders on three data sets representing different domains. Automatic and human evaluation demonstrate that our summaries have better content coverage.

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Sentence Centrality Revisited for Unsupervised Summarization
Hao Zheng | Mirella Lapata
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Single document summarization has enjoyed renewed interest in recent years thanks to the popularity of neural network models and the availability of large-scale datasets. In this paper we develop an unsupervised approach arguing that it is unrealistic to expect large-scale and high-quality training data to be available or created for different types of summaries, domains, or languages. We revisit a popular graph-based ranking algorithm and modify how node (aka sentence) centrality is computed in two ways: (a) we employ BERT, a state-of-the-art neural representation learning model to better capture sentential meaning and (b) we build graphs with directed edges arguing that the contribution of any two nodes to their respective centrality is influenced by their relative position in a document. Experimental results on three news summarization datasets representative of different languages and writing styles show that our approach outperforms strong baselines by a wide margin.

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Discourse Representation Parsing for Sentences and Documents
Jiangming Liu | Shay B. Cohen | Mirella Lapata
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We introduce a novel semantic parsing task based on Discourse Representation Theory (DRT; Kamp and Reyle 1993). Our model operates over Discourse Representation Tree Structures which we formally define for sentences and documents. We present a general framework for parsing discourse structures of arbitrary length and granularity. We achieve this with a neural model equipped with a supervised hierarchical attention mechanism and a linguistically-motivated copy strategy. Experimental results on sentence- and document-level benchmarks show that our model outperforms competitive baselines by a wide margin.

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Learning an Executable Neural Semantic Parser
Jianpeng Cheng | Siva Reddy | Vijay Saraswat | Mirella Lapata
Computational Linguistics, Volume 45, Issue 1 - March 2019

This article describes a neural semantic parser that maps natural language utterances onto logical forms that can be executed against a task-specific environment, such as a knowledge base or a database, to produce a response. The parser generates tree-structured logical forms with a transition-based approach, combining a generic tree-generation algorithm with domain-general grammar defined by the logical language. The generation process is modeled by structured recurrent neural networks, which provide a rich encoding of the sentential context and generation history for making predictions. To tackle mismatches between natural language and logical form tokens, various attention mechanisms are explored. Finally, we consider different training settings for the neural semantic parser, including fully supervised training where annotated logical forms are given, weakly supervised training where denotations are provided, and distant supervision where only unlabeled sentences and a knowledge base are available. Experiments across a wide range of data sets demonstrate the effectiveness of our parser.

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Single Document Summarization as Tree Induction
Yang Liu | Ivan Titov | Mirella Lapata
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

In this paper, we conceptualize single-document extractive summarization as a tree induction problem. In contrast to previous approaches which have relied on linguistically motivated document representations to generate summaries, our model induces a multi-root dependency tree while predicting the output summary. Each root node in the tree is a summary sentence, and the subtrees attached to it are sentences whose content relates to or explains the summary sentence. We design a new iterative refinement algorithm: it induces the trees through repeatedly refining the structures predicted by previous iterations. We demonstrate experimentally on two benchmark datasets that our summarizer performs competitively against state-of-the-art methods.

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Text Generation from Knowledge Graphs with Graph Transformers
Rik Koncel-Kedziorski | Dhanush Bekal | Yi Luan | Mirella Lapata | Hannaneh Hajishirzi
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce. In this work, we address the problem of generating coherent multi-sentence texts from the output of an information extraction system, and in particular a knowledge graph. Graphical knowledge representations are ubiquitous in computing, but pose a significant challenge for text generation techniques due to their non-hierarchical nature, collapsing of long-distance dependencies, and structural variety. We introduce a novel graph transforming encoder which can leverage the relational structure of such knowledge graphs without imposing linearization or hierarchical constraints. Incorporated into an encoder-decoder setup, we provide an end-to-end trainable system for graph-to-text generation that we apply to the domain of scientific text. Automatic and human evaluations show that our technique produces more informative texts which exhibit better document structure than competitive encoder-decoder methods.

2018

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Weakly-Supervised Neural Semantic Parsing with a Generative Ranker
Jianpeng Cheng | Mirella Lapata
Proceedings of the 22nd Conference on Computational Natural Language Learning

Weakly-supervised semantic parsers are trained on utterance-denotation pairs, treating logical forms as latent. The task is challenging due to the large search space and spuriousness of logical forms. In this paper we introduce a neural parser-ranker system for weakly-supervised semantic parsing. The parser generates candidate tree-structured logical forms from utterances using clues of denotations. These candidates are then ranked based on two criterion: their likelihood of executing to the correct denotation, and their agreement with the utterance semantics. We present a scheduled training procedure to balance the contribution of the two objectives. Furthermore, we propose to use a neurally encoded lexicon to inject prior domain knowledge to the model. Experiments on three Freebase datasets demonstrate the effectiveness of our semantic parser, achieving results within the state-of-the-art range.

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Bootstrapping Generators from Noisy Data
Laura Perez-Beltrachini | Mirella Lapata
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and associated texts. In this paper we aim to bootstrap generators from large scale datasets where the data (e.g., DBPedia facts) and related texts (e.g., Wikipedia abstracts) are loosely aligned. We tackle this challenging task by introducing a special-purpose content selection mechanism. We use multi-instance learning to automatically discover correspondences between data and text pairs and show how these can be used to enhance the content signal while training an encoder-decoder architecture. Experimental results demonstrate that models trained with content-specific objectives improve upon a vanilla encoder-decoder which solely relies on soft attention.

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Ranking Sentences for Extractive Summarization with Reinforcement Learning
Shashi Narayan | Shay B. Cohen | Mirella Lapata
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Single document summarization is the task of producing a shorter version of a document while preserving its principal information content. In this paper we conceptualize extractive summarization as a sentence ranking task and propose a novel training algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective. We use our algorithm to train a neural summarization model on the CNN and DailyMail datasets and demonstrate experimentally that it outperforms state-of-the-art extractive and abstractive systems when evaluated automatically and by humans.

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What’s This Movie About? A Joint Neural Network Architecture for Movie Content Analysis
Philip John Gorinski | Mirella Lapata
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

This work takes a first step toward movie content analysis by tackling the novel task of movie overview generation. Overviews are natural language texts that give a first impression of a movie, describing aspects such as its genre, plot, mood, or artistic style. We create a dataset that consists of movie scripts, attribute-value pairs for the movies’ aspects, as well as overviews, which we extract from an online database. We present a novel end-to-end model for overview generation, consisting of a multi-label encoder for identifying screenplay attributes, and an LSTM decoder to generate natural language sentences conditioned on the identified attributes. Automatic and human evaluation show that the encoder is able to reliably assign good labels for the movie’s attributes, and the overviews provide descriptions of the movie’s content which are informative and faithful.

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Whodunnit? Crime Drama as a Case for Natural Language Understanding
Lea Frermann | Shay B. Cohen | Mirella Lapata
Transactions of the Association for Computational Linguistics, Volume 6

In this paper we argue that crime drama exemplified in television programs such as CSI: Crime Scene Investigation is an ideal testbed for approximating real-world natural language understanding and the complex inferences associated with it. We propose to treat crime drama as a new inference task, capitalizing on the fact that each episode poses the same basic question (i.e., who committed the crime) and naturally provides the answer when the perpetrator is revealed. We develop a new dataset based on CSI episodes, formalize perpetrator identification as a sequence labeling problem, and develop an LSTM-based model which learns from multi-modal data. Experimental results show that an incremental inference strategy is key to making accurate guesses as well as learning from representations fusing textual, visual, and acoustic input.

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Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis
Stefanos Angelidis | Mirella Lapata
Transactions of the Association for Computational Linguistics, Volume 6

We consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL). Our neural model is trained on document sentiment labels, and learns to predict the sentiment of text segments, i.e. sentences or elementary discourse units (EDUs), without segment-level supervision. We introduce an attention-based polarity scoring method for identifying positive and negative text snippets and a new dataset which we call SpoT (as shorthand for Segment-level POlariTy annotations) for evaluating MIL-style sentiment models like ours. Experimental results demonstrate superior performance against multiple baselines, whereas a judgement elicitation study shows that EDU-level opinion extraction produces more informative summaries than sentence-based alternatives.

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Learning Structured Text Representations
Yang Liu | Mirella Lapata
Transactions of the Association for Computational Linguistics, Volume 6

In this paper, we focus on learning structure-aware document representations from data without recourse to a discourse parser or additional annotations. Drawing inspiration from recent efforts to empower neural networks with a structural bias (Cheng et al., 2016; Kim et al., 2017), we propose a model that can encode a document while automatically inducing rich structural dependencies. Specifically, we embed a differentiable non-projective parsing algorithm into a neural model and use attention mechanisms to incorporate the structural biases. Experimental evaluations across different tasks and datasets show that the proposed model achieves state-of-the-art results on document modeling tasks while inducing intermediate structures which are both interpretable and meaningful.

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Neural Latent Extractive Document Summarization
Xingxing Zhang | Mirella Lapata | Furu Wei | Ming Zhou
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Extractive summarization models need sentence level labels, which are usually created with rule-based methods since most summarization datasets only have document summary pairs. These labels might be suboptimal. We propose a latent variable extractive model, where sentences are viewed as latent variables and sentences with activated variables are used to infer gold summaries. During training, the loss can come directly from gold summaries. Experiments on CNN/Dailymail dataset show our latent extractive model outperforms a strong extractive baseline trained on rule-based labels and also performs competitively with several recent models.

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Structured Alignment Networks for Matching Sentences
Yang Liu | Matt Gardner | Mirella Lapata
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Many tasks in natural language processing involve comparing two sentences to compute some notion of relevance, entailment, or similarity. Typically this comparison is done either at the word level or at the sentence level, with no attempt to leverage the inherent structure of the sentence. When sentence structure is used for comparison, it is obtained during a non-differentiable pre-processing step, leading to propagation of errors. We introduce a model of structured alignments between sentences, showing how to compare two sentences by matching their latent structures. Using a structured attention mechanism, our model matches candidate spans in the first sentence to candidate spans in the second sentence, simultaneously discovering the tree structure of each sentence. Our model is fully differentiable and trained only on the matching objective. We evaluate this model on two tasks, natural entailment detection and answer sentence selection, and find that modeling latent tree structures results in superior performance. Analysis of the learned sentence structures shows they can reflect some syntactic phenomena.

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Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
Shashi Narayan | Shay B. Cohen | Mirella Lapata
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We introduce “extreme summarization”, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach. The idea is to create a short, one-sentence news summary answering the question “What is the article about?”. We collect a real-world, large-scale dataset for this task by harvesting online articles from the British Broadcasting Corporation (BBC). We propose a novel abstractive model which is conditioned on the article’s topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans.

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Sentence Compression for Arbitrary Languages via Multilingual Pivoting
Jonathan Mallinson | Rico Sennrich | Mirella Lapata
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

In this paper we advocate the use of bilingual corpora which are abundantly available for training sentence compression models. Our approach borrows much of its machinery from neural machine translation and leverages bilingual pivoting: compressions are obtained by translating a source string into a foreign language and then back-translating it into the source while controlling the translation length. Our model can be trained for any language as long as a bilingual corpus is available and performs arbitrary rewrites without access to compression specific data. We release. Moss, a new parallel Multilingual Compression dataset for English, German, and French which can be used to evaluate compression models across languages and genres.

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Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised
Stefanos Angelidis | Mirella Lapata
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We present a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision (e.g., in the form of product domain labels and user-provided ratings). Our method combines two weakly supervised components to identify salient opinions and form extractive summaries from multiple reviews: an aspect extractor trained under a multi-task objective, and a sentiment predictor based on multiple instance learning. We introduce an opinion summarization dataset that includes a training set of product reviews from six diverse domains and human-annotated development and test sets with gold standard aspect annotations, salience labels, and opinion summaries. Automatic evaluation shows significant improvements over baselines, and a large-scale study indicates that our opinion summaries are preferred by human judges according to multiple criteria.

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Discourse Representation Structure Parsing
Jiangming Liu | Shay B. Cohen | Mirella Lapata
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We introduce an open-domain neural semantic parser which generates formal meaning representations in the style of Discourse Representation Theory (DRT; Kamp and Reyle 1993). We propose a method which transforms Discourse Representation Structures (DRSs) to trees and develop a structure-aware model which decomposes the decoding process into three stages: basic DRS structure prediction, condition prediction (i.e., predicates and relations), and referent prediction (i.e., variables). Experimental results on the Groningen Meaning Bank (GMB) show that our model outperforms competitive baselines by a wide margin.

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Coarse-to-Fine Decoding for Neural Semantic Parsing
Li Dong | Mirella Lapata
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Semantic parsing aims at mapping natural language utterances into structured meaning representations. In this work, we propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. Given an input utterance, we first generate a rough sketch of its meaning, where low-level information (such as variable names and arguments) is glossed over. Then, we fill in missing details by taking into account the natural language input and the sketch itself. Experimental results on four datasets characteristic of different domains and meaning representations show that our approach consistently improves performance, achieving competitive results despite the use of relatively simple decoders.

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Confidence Modeling for Neural Semantic Parsing
Li Dong | Chris Quirk | Mirella Lapata
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this work we focus on confidence modeling for neural semantic parsers which are built upon sequence-to-sequence models. We outline three major causes of uncertainty, and design various metrics to quantify these factors. These metrics are then used to estimate confidence scores that indicate whether model predictions are likely to be correct. Beyond confidence estimation, we identify which parts of the input contribute to uncertain predictions allowing users to interpret their model, and verify or refine its input. Experimental results show that our confidence model significantly outperforms a widely used method that relies on posterior probability, and improves the quality of interpretation compared to simply relying on attention scores.

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Document Modeling with External Attention for Sentence Extraction
Shashi Narayan | Ronald Cardenas | Nikos Papasarantopoulos | Shay B. Cohen | Mirella Lapata | Jiangsheng Yu | Yi Chang
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Document modeling is essential to a variety of natural language understanding tasks. We propose to use external information to improve document modeling for problems that can be framed as sentence extraction. We develop a framework composed of a hierarchical document encoder and an attention-based extractor with attention over external information. We evaluate our model on extractive document summarization (where the external information is image captions and the title of the document) and answer selection (where the external information is a question). We show that our model consistently outperforms strong baselines, in terms of both informativeness and fluency (for CNN document summarization) and achieves state-of-the-art results for answer selection on WikiQA and NewsQA.

2017

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Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Mirella Lapata | Phil Blunsom | Alexander Koller
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

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Learning to Generate Product Reviews from Attributes
Li Dong | Shaohan Huang | Furu Wei | Mirella Lapata | Ming Zhou | Ke Xu
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Automatically generating product reviews is a meaningful, yet not well-studied task in sentiment analysis. Traditional natural language generation methods rely extensively on hand-crafted rules and predefined templates. This paper presents an attention-enhanced attribute-to-sequence model to generate product reviews for given attribute information, such as user, product, and rating. The attribute encoder learns to represent input attributes as vectors. Then, the sequence decoder generates reviews by conditioning its output on these vectors. We also introduce an attention mechanism to jointly generate reviews and align words with input attributes. The proposed model is trained end-to-end to maximize the likelihood of target product reviews given the attributes. We build a publicly available dataset for the review generation task by leveraging the Amazon book reviews and their metadata. Experiments on the dataset show that our approach outperforms baseline methods and the attention mechanism significantly improves the performance of our model.

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Dependency Parsing as Head Selection
Xingxing Zhang | Jianpeng Cheng | Mirella Lapata
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Conventional graph-based dependency parsers guarantee a tree structure both during training and inference. Instead, we formalize dependency parsing as the problem of independently selecting the head of each word in a sentence. Our model which we call DENSE (as shorthand for Dependency Neural Selection) produces a distribution over possible heads for each word using features obtained from a bidirectional recurrent neural network. Without enforcing structural constraints during training, DeNSe generates (at inference time) trees for the overwhelming majority of sentences, while non-tree outputs can be adjusted with a maximum spanning tree algorithm. We evaluate DeNSe on four languages (English, Chinese, Czech, and German) with varying degrees of non-projectivity. Despite the simplicity of the approach, our parsers are on par with the state of the art.

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Paraphrasing Revisited with Neural Machine Translation
Jonathan Mallinson | Rico Sennrich | Mirella Lapata
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Recognizing and generating paraphrases is an important component in many natural language processing applications. A well-established technique for automatically extracting paraphrases leverages bilingual corpora to find meaning-equivalent phrases in a single language by “pivoting” over a shared translation in another language. In this paper we revisit bilingual pivoting in the context of neural machine translation and present a paraphrasing model based purely on neural networks. Our model represents paraphrases in a continuous space, estimates the degree of semantic relatedness between text segments of arbitrary length, and generates candidate paraphrases for any source input. Experimental results across tasks and datasets show that neural paraphrases outperform those obtained with conventional phrase-based pivoting approaches.

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Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
Mirella Lapata | Phil Blunsom | Alexander Koller
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

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Learning Structured Natural Language Representations for Semantic Parsing
Jianpeng Cheng | Siva Reddy | Vijay Saraswat | Mirella Lapata
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We introduce a neural semantic parser which is interpretable and scalable. Our model converts natural language utterances to intermediate, domain-general natural language representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. The semantic parser is trained end-to-end using annotated logical forms or their denotations. We achieve the state of the art on SPADES and GRAPHQUESTIONS and obtain competitive results on GEOQUERY and WEBQUESTIONS. The induced predicate-argument structures shed light on the types of representations useful for semantic parsing and how these are different from linguistically motivated ones.

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A Generative Parser with a Discriminative Recognition Algorithm
Jianpeng Cheng | Adam Lopez | Mirella Lapata
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Generative models defining joint distributions over parse trees and sentences are useful for parsing and language modeling, but impose restrictions on the scope of features and are often outperformed by discriminative models. We propose a framework for parsing and language modeling which marries a generative model with a discriminative recognition model in an encoder-decoder setting. We provide interpretations of the framework based on expectation maximization and variational inference, and show that it enables parsing and language modeling within a single implementation. On the English Penn Treen-bank, our framework obtains competitive performance on constituency parsing while matching the state-of-the-art single-model language modeling score.

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Universal Semantic Parsing
Siva Reddy | Oscar Täckström | Slav Petrov | Mark Steedman | Mirella Lapata
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Universal Dependencies (UD) offer a uniform cross-lingual syntactic representation, with the aim of advancing multilingual applications. Recent work shows that semantic parsing can be accomplished by transforming syntactic dependencies to logical forms. However, this work is limited to English, and cannot process dependency graphs, which allow handling complex phenomena such as control. In this work, we introduce UDepLambda, a semantic interface for UD, which maps natural language to logical forms in an almost language-independent fashion and can process dependency graphs. We perform experiments on question answering against Freebase and provide German and Spanish translations of the WebQuestions and GraphQuestions datasets to facilitate multilingual evaluation. Results show that UDepLambda outperforms strong baselines across languages and datasets. For English, it achieves a 4.9 F1 point improvement over the state-of-the-art on GraphQuestions.

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Sentence Simplification with Deep Reinforcement Learning
Xingxing Zhang | Mirella Lapata
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Sentence simplification aims to make sentences easier to read and understand. Most recent approaches draw on insights from machine translation to learn simplification rewrites from monolingual corpora of complex and simple sentences. We address the simplification problem with an encoder-decoder model coupled with a deep reinforcement learning framework. Our model, which we call DRESS (as shorthand for Deep REinforcement Sentence Simplification), explores the space of possible simplifications while learning to optimize a reward function that encourages outputs which are simple, fluent, and preserve the meaning of the input. Experiments on three datasets demonstrate that our model outperforms competitive simplification systems.

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Learning to Paraphrase for Question Answering
Li Dong | Jonathan Mallinson | Siva Reddy | Mirella Lapata
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Question answering (QA) systems are sensitive to the many different ways natural language expresses the same information need. In this paper we turn to paraphrases as a means of capturing this knowledge and present a general framework which learns felicitous paraphrases for various QA tasks. Our method is trained end-to-end using question-answer pairs as a supervision signal. A question and its paraphrases serve as input to a neural scoring model which assigns higher weights to linguistic expressions most likely to yield correct answers. We evaluate our approach on QA over Freebase and answer sentence selection. Experimental results on three datasets show that our framework consistently improves performance, achieving competitive results despite the use of simple QA models.

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Learning Contextually Informed Representations for Linear-Time Discourse Parsing
Yang Liu | Mirella Lapata
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Recent advances in RST discourse parsing have focused on two modeling paradigms: (a) high order parsers which jointly predict the tree structure of the discourse and the relations it encodes; or (b) linear-time parsers which are efficient but mostly based on local features. In this work, we propose a linear-time parser with a novel way of representing discourse constituents based on neural networks which takes into account global contextual information and is able to capture long-distance dependencies. Experimental results show that our parser obtains state-of-the art performance on benchmark datasets, while being efficient (with time complexity linear in the number of sentences in the document) and requiring minimal feature engineering.

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Image Pivoting for Learning Multilingual Multimodal Representations
Spandana Gella | Rico Sennrich | Frank Keller | Mirella Lapata
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

In this paper we propose a model to learn multimodal multilingual representations for matching images and sentences in different languages, with the aim of advancing multilingual versions of image search and image understanding. Our model learns a common representation for images and their descriptions in two different languages (which need not be parallel) by considering the image as a pivot between two languages. We introduce a new pairwise ranking loss function which can handle both symmetric and asymmetric similarity between the two modalities. We evaluate our models on image-description ranking for German and English, and on semantic textual similarity of image descriptions in English. In both cases we achieve state-of-the-art performance.

2016

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Language to Logical Form with Neural Attention
Li Dong | Mirella Lapata
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Neural Summarization by Extracting Sentences and Words
Jianpeng Cheng | Mirella Lapata
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Neural Semantic Role Labeling with Dependency Path Embeddings
Michael Roth | Mirella Lapata
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings
Spandana Gella | Mirella Lapata | Frank Keller
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Top-down Tree Long Short-Term Memory Networks
Xingxing Zhang | Liang Lu | Mirella Lapata
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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A Bayesian Model of Diachronic Meaning Change
Lea Frermann | Mirella Lapata
Transactions of the Association for Computational Linguistics, Volume 4

Word meanings change over time and an automated procedure for extracting this information from text would be useful for historical exploratory studies, information retrieval or question answering. We present a dynamic Bayesian model of diachronic meaning change, which infers temporal word representations as a set of senses and their prevalence. Unlike previous work, we explicitly model language change as a smooth, gradual process. We experimentally show that this modeling decision is beneficial: our model performs competitively on meaning change detection tasks whilst inducing discernible word senses and their development over time. Application of our model to the SemEval-2015 temporal classification benchmark datasets further reveals that it performs on par with highly optimized task-specific systems.

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Transforming Dependency Structures to Logical Forms for Semantic Parsing
Siva Reddy | Oscar Täckström | Michael Collins | Tom Kwiatkowski | Dipanjan Das | Mark Steedman | Mirella Lapata
Transactions of the Association for Computational Linguistics, Volume 4

The strongly typed syntax of grammar formalisms such as CCG, TAG, LFG and HPSG offers a synchronous framework for deriving syntactic structures and semantic logical forms. In contrast—partly due to the lack of a strong type system—dependency structures are easy to annotate and have become a widely used form of syntactic analysis for many languages. However, the lack of a type system makes a formal mechanism for deriving logical forms from dependency structures challenging. We address this by introducing a robust system based on the lambda calculus for deriving neo-Davidsonian logical forms from dependency trees. These logical forms are then used for semantic parsing of natural language to Freebase. Experiments on the Free917 and Web-Questions datasets show that our representation is superior to the original dependency trees and that it outperforms a CCG-based representation on this task. Compared to prior work, we obtain the strongest result to date on Free917 and competitive results on WebQuestions.

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Long Short-Term Memory-Networks for Machine Reading
Jianpeng Cheng | Li Dong | Mirella Lapata
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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Movie Script Summarization as Graph-based Scene Extraction
Philip John Gorinski | Mirella Lapata
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Learning to Interpret and Describe Abstract Scenes
Luis Gilberto Mateos Ortiz | Clemens Wolff | Mirella Lapata
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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A Bayesian Model for Joint Learning of Categories and their Features
Lea Frermann | Mirella Lapata
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Which Step Do I Take First? Troubleshooting with Bayesian Models
Annie Louis | Mirella Lapata
Transactions of the Association for Computational Linguistics, Volume 3

Online discussion forums and community question-answering websites provide one of the primary avenues for online users to share information. In this paper, we propose text mining techniques which aid users navigate troubleshooting-oriented data such as questions asked on forums and their suggested solutions. We introduce Bayesian generative models of the troubleshooting data and apply them to two interrelated tasks: (a) predicting the complexity of the solutions (e.g., plugging a keyboard in the computer is easier compared to installing a special driver) and (b) presenting them in a ranked order from least to most complex. Experimental results show that our models are on par with human performance on these tasks, while outperforming baselines based on solution length or readability.

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Context-aware Frame-Semantic Role Labeling
Michael Roth | Mirella Lapata
Transactions of the Association for Computational Linguistics, Volume 3

Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis. Predicting such representations from raw text is, however, a challenging task and corresponding models are typically only trained on a small set of sentence-level annotations. In this paper, we present a semantic role labeling system that takes into account sentence and discourse context. We introduce several new features which we motivate based on linguistic insights and experimentally demonstrate that they lead to significant improvements over the current state-of-the-art in FrameNet-based semantic role labeling.

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Distributed Representations for Unsupervised Semantic Role Labeling
Kristian Woodsend | Mirella Lapata
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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Similarity-Driven Semantic Role Induction via Graph Partitioning
Joel Lang | Mirella Lapata
Computational Linguistics, Volume 40, Issue 3 - September 2014

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Learning Grounded Meaning Representations with Autoencoders
Carina Silberer | Mirella Lapata
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Large-scale Semantic Parsing without Question-Answer Pairs
Siva Reddy | Mirella Lapata | Mark Steedman
Transactions of the Association for Computational Linguistics, Volume 2

In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs. Our key insight is to represent natural language via semantic graphs whose topology shares many commonalities with Freebase. Given this representation, we conceptualize semantic parsing as a graph matching problem. Our model converts sentences to semantic graphs using CCG and subsequently grounds them to Freebase guided by denotations as a form of weak supervision. Evaluation experiments on a subset of the Free917 and WebQuestions benchmark datasets show our semantic parser improves over the state of the art.

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Incremental Semantic Role Labeling with Tree Adjoining Grammar
Ioannis Konstas | Frank Keller | Vera Demberg | Mirella Lapata
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Chinese Poetry Generation with Recurrent Neural Networks
Xingxing Zhang | Mirella Lapata
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Incremental Bayesian Learning of Semantic Categories
Lea Frermann | Mirella Lapata
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

2013

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Unsupervised Relation Extraction with General Domain Knowledge
Oier Lopez de Lacalle | Mirella Lapata
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Inducing Document Plans for Concept-to-Text Generation
Ioannis Konstas | Mirella Lapata
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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A Quantum-Theoretic Approach to Distributional Semantics
William Blacoe | Elham Kashefi | Mirella Lapata
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Unsupervised Relation Extraction with General Domain Knowledge
Mirella Lapata
Proceedings of the Joint Symposium on Semantic Processing. Textual Inference and Structures in Corpora

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Semantic v.s. Positions: Utilizing Balanced Proximity in Language Model Smoothing for Information Retrieval
Rui Yan | Han Jiang | Mirella Lapata | Shou-De Lin | Xueqiang Lv | Xiaoming Li
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Models of Semantic Representation with Visual Attributes
Carina Silberer | Vittorio Ferrari | Mirella Lapata
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Semi-Supervised Semantic Role Labeling via Structural Alignment
Hagen Fürstenau | Mirella Lapata
Computational Linguistics, Volume 38, Issue 1 - March 2012

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Concept-to-text Generation via Discriminative Reranking
Ioannis Konstas | Mirella Lapata
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Tweet Recommendation with Graph Co-Ranking
Rui Yan | Mirella Lapata | Xiaoming Li
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Taxonomy Induction Using Hierarchical Random Graphs
Trevor Fountain | Mirella Lapata
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Unsupervised Concept-to-text Generation with Hypergraphs
Ioannis Konstas | Mirella Lapata
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Multiple Aspect Summarization Using Integer Linear Programming
Kristian Woodsend | Mirella Lapata
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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A Comparison of Vector-based Representations for Semantic Composition
William Blacoe | Mirella Lapata
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Grounded Models of Semantic Representation
Carina Silberer | Mirella Lapata
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

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Unsupervised Semantic Role Induction via Split-Merge Clustering
Joel Lang | Mirella Lapata
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Learning to Simplify Sentences with Quasi-Synchronous Grammar and Integer Programming
Kristian Woodsend | Mirella Lapata
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Unsupervised Semantic Role Induction with Graph Partitioning
Joel Lang | Mirella Lapata
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2010

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Topic Models for Meaning Similarity in Context
Georgiana Dinu | Mirella Lapata
Coling 2010: Posters

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Discourse Constraints for Document Compression
James Clarke | Mirella Lapata
Computational Linguistics, Volume 36, Issue 3 - September 2010

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Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Mirella Lapata | Anoop Sarkar
Proceedings of the Fourteenth Conference on Computational Natural Language Learning

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Syntactic and Semantic Factors in Processing Difficulty: An Integrated Measure
Jeff Mitchell | Mirella Lapata | Vera Demberg | Frank Keller
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Automatic Generation of Story Highlights
Kristian Woodsend | Mirella Lapata
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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How Many Words Is a Picture Worth? Automatic Caption Generation for News Images
Yansong Feng | Mirella Lapata
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Plot Induction and Evolutionary Search for Story Generation
Neil McIntyre | Mirella Lapata
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Visual Information in Semantic Representation
Yansong Feng | Mirella Lapata
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Topic Models for Image Annotation and Text Illustration
Yansong Feng | Mirella Lapata
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Unsupervised Induction of Semantic Roles
Joel Lang | Mirella Lapata
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Title Generation with Quasi-Synchronous Grammar
Kristian Woodsend | Yansong Feng | Mirella Lapata
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Measuring Distributional Similarity in Context
Georgiana Dinu | Mirella Lapata
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

2009

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Graph Alignment for Semi-Supervised Semantic Role Labeling
Hagen Fürstenau | Mirella Lapata
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Language Models Based on Semantic Composition
Jeff Mitchell | Mirella Lapata
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Bayesian Word Sense Induction
Samuel Brody | Mirella Lapata
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Semi-Supervised Semantic Role Labeling
Hagen Fürstenau | Mirella Lapata
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Learning to Tell Tales: A Data-driven Approach to Story Generation
Neil McIntyre | Mirella Lapata
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

2008

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Good Neighbors Make Good Senses: Exploiting Distributional Similarity for Unsupervised WSD
Samuel Brody | Mirella Lapata
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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ParaMetric: An Automatic Evaluation Metric for Paraphrasing
Chris Callison-Burch | Trevor Cohn | Mirella Lapata
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Sentence Compression Beyond Word Deletion
Trevor Cohn | Mirella Lapata
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing
Mirella Lapata | Hwee Tou Ng
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

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Vector-based Models of Semantic Composition
Jeff Mitchell | Mirella Lapata
Proceedings of ACL-08: HLT

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Automatic Image Annotation Using Auxiliary Text Information
Yansong Feng | Mirella Lapata
Proceedings of ACL-08: HLT

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Modeling Local Coherence: An Entity-Based Approach
Regina Barzilay | Mirella Lapata
Computational Linguistics, Volume 34, Number 1, March 2008

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Constructing Corpora for the Development and Evaluation of Paraphrase Systems
Trevor Cohn | Chris Callison-Burch | Mirella Lapata
Computational Linguistics, Volume 34, Number 4, December 2008

2007

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An Information Retrieval Approach to Sense Ranking
Mirella Lapata | Frank Keller
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

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Machine Translation by Triangulation: Making Effective Use of Multi-Parallel Corpora
Trevor Cohn | Mirella Lapata
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

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Dependency-Based Construction of Semantic Space Models
Sebastian Padó | Mirella Lapata
Computational Linguistics, Volume 33, Number 2, June 2007

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Modelling Compression with Discourse Constraints
James Clarke | Mirella Lapata
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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Using Semantic Roles to Improve Question Answering
Dan Shen | Mirella Lapata
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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Large Margin Synchronous Generation and its Application to Sentence Compression
Trevor Cohn | Mirella Lapata
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

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Aggregation via Set Partitioning for Natural Language Generation
Regina Barzilay | Mirella Lapata
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

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Automatic Evaluation of Information Ordering: Kendall’s Tau
Mirella Lapata
Computational Linguistics, Volume 32, Number 4, December 2006

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Ensemble Methods for Unsupervised WSD
Samuel Brody | Roberto Navigli | Mirella Lapata
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Models for Sentence Compression: A Comparison across Domains, Training Requirements and Evaluation Measures
James Clarke | Mirella Lapata
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Optimal Constituent Alignment with Edge Covers for Semantic Projection
Sebastian Padó | Mirella Lapata
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Constraint-Based Sentence Compression: An Integer Programming Approach
James Clarke | Mirella Lapata
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

2005

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Modeling Local Coherence: An Entity-Based Approach
Regina Barzilay | Mirella Lapata
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

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Discourse Chunking and its Application to Sentence Compression
Caroline Sporleder | Mirella Lapata
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

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Collective Content Selection for Concept-to-Text Generation
Regina Barzilay | Mirella Lapata
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

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Cross-linguistic Projection of Role-Semantic Information
Sebastian Padó | Mirella Lapata
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

2004

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Automatic Paragraph Identification: A Study across Languages and Domains
Caroline Sporleder | Mirella Lapata
Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing

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The Web as a Baseline: Evaluating the Performance of Unsupervised Web-based Models for a Range of NLP Tasks
Mirella Lapata | Frank Keller
Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004

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Inferring Sentence-internal Temporal Relations
Mirella Lapata | Alex Lascarides
Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004

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Verb Class Disambiguation Using Informative Priors
Mirella Lapata | Chris Brew
Computational Linguistics, Volume 30, Number 1, March 2004

2003

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Constructing Semantic Space Models from Parsed Corpora
Sebastian Padó | Mirella Lapata
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

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Probabilistic Text Structuring: Experiments with Sentence Ordering
Mirella Lapata
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

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Using the Web to Obtain Frequencies for Unseen Bigrams
Frank Keller | Mirella Lapata
Computational Linguistics, Volume 29, Number 3, September 2003: Special Issue on the Web as Corpus

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Evaluating and Combining Approaches to Selectional Preference Acquisition
Carsten Brockmann | Mirella Lapata
10th Conference of the European Chapter of the Association for Computational Linguistics

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Detecting Novel Compounds: The Role of Distributional Evidence
Mirella Lapata | Alex Lascarides
10th Conference of the European Chapter of the Association for Computational Linguistics

2002

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XML-based NLP Tools for Analysing and Annotating Medical Language
Claire Grover | Ewan Klein | Mirella Lapata | Alex Lascarides
COLING-02: The 2nd Workshop on NLP and XML (NLPXML-2002)

1997

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Expanding the Domain of a Multi-lingual Speech-to-Speech Translation System
Alon Lavie | Lori Levin | Puming Zhan | Maite Taboada | Donna Gates | Mirella Lapata | Cortis Clark | Matthew Broadhead | Alex Waibel
Spoken Language Translation

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