Shashi Narayan


2022

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Data Augmentation for Low-Resource Dialogue Summarization
Yongtai Liu | Joshua Maynez | Gonçalo Simões | Shashi Narayan
Findings of the Association for Computational Linguistics: NAACL 2022

We present DADS, a novel Data Augmentation technique for low-resource Dialogue Summarization. Our method generates synthetic examples by replacing sections of text from both the input dialogue and summary while preserving the augmented summary to correspond to a viable summary for the augmented dialogue. We utilize pretrained language models that produce highly likely dialogue alternatives while still being free to generate diverse alternatives. We applied our data augmentation method to the SAMSum dataset in low resource scenarios, mimicking real world problems such as chat, thread, and meeting summarization where large scale supervised datasets with human-written summaries are scarce. Through both automatic and human evaluations, we show that DADS shows strong improvements for low resource scenarios while generating topically diverse summaries without introducing additional hallucinations to the summaries.

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A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation
Shashi Narayan | Gonçalo Simões | Yao Zhao | Joshua Maynez | Dipanjan Das | Michael Collins | Mirella Lapata
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We propose Composition Sampling, a simple but effective method to generate diverse outputs for conditional generation of higher quality compared to previous stochastic decoding strategies. It builds on recently proposed plan-based neural generation models (FROST, Narayan et al, 2021) that are trained to first create a composition of the output and then generate by conditioning on it and the input. Our approach avoids text degeneration by first sampling a composition in the form of an entity chain and then using beam search to generate the best possible text grounded to this entity chain. Experiments on summarization (CNN/DailyMail and XSum) and question generation (SQuAD), using existing and newly proposed automaticmetrics together with human-based evaluation, demonstrate that Composition Sampling is currently the best available decoding strategy for generating diverse meaningful outputs.

2021

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A Thorough Evaluation of Task-Specific Pretraining for Summarization
Sascha Rothe | Joshua Maynez | Shashi Narayan
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Task-agnostic pretraining objectives like masked language models or corrupted span prediction are applicable to a wide range of NLP downstream tasks (Raffel et al.,2019), but are outperformed by task-specific pretraining objectives like predicting extracted gap sentences on summarization (Zhang et al.,2020). We compare three summarization specific pretraining objectives with the task agnostic corrupted span prediction pretraining in controlled study. We also extend our study to a low resource and zero shot setup, to understand how many training examples are needed in order to ablate the task-specific pretraining without quality loss. Our results show that task-agnostic pretraining is sufficient for most cases which hopefully reduces the need for costly task-specific pretraining. We also report new state-of-the-art number for two summarization task using a T5 model with 11 billion parameters and an optimal beam search length penalty.

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MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization
Xinnuo Xu | Ondřej Dušek | Shashi Narayan | Verena Rieser | Ioannis Konstas
Findings of the Association for Computational Linguistics: EMNLP 2021

One of the most challenging aspects of current single-document news summarization is that the summary often contains ‘extrinsic hallucinations’, i.e., facts that are not present in the source document, which are often derived via world knowledge. This causes summarisation systems to act more like open-ended language models tending to hallucinate facts that are erroneous. In this paper, we mitigate this problem with the help of multiple supplementary resource documents assisting the task. We present a new dataset MiraNews and benchmark existing summarisation models. In contrast to multi-document summarization, which addresses multiple events from several source documents, we still aim at generating a summary for a single document. We show via data analysis that it’s not only the models which are to blame: more than 27% of facts mentioned in the gold summaries of MiraNews are better grounded on assisting documents than in the main source articles. An error analysis of generated summaries from pretrained models fine-tuned on MIRANEWS reveals that this has an even bigger effects on models: assisted summarisation reduces 55% of hallucinations when compared to single-document summarisation models trained on the main article only.

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Planning with Learned Entity Prompts for Abstractive Summarization
Shashi Narayan | Yao Zhao | Joshua Maynez | Gonçalo Simões | Vitaly Nikolaev | Ryan McDonald
Transactions of the Association for Computational Linguistics, Volume 9

Abstract We introduce a simple but flexible mechanism to learn an intermediate plan to ground the generation of abstractive summaries. Specifically, we prepend (or prompt) target summaries with entity chains—ordered sequences of entities mentioned in the summary. Transformer-based sequence-to-sequence models are then trained to generate the entity chain and then continue generating the summary conditioned on the entity chain and the input. We experimented with both pretraining and finetuning with this content planning objective. When evaluated on CNN/DailyMail, XSum, SAMSum, and BillSum, we demonstrate empirically that the grounded generation with the planning objective improves entity specificity and planning in summaries for all datasets, and achieves state-of-the-art performance on XSum and SAMSum in terms of rouge. Moreover, we demonstrate empirically that planning with entity chains provides a mechanism to control hallucinations in abstractive summaries. By prompting the decoder with a modified content plan that drops hallucinated entities, we outperform state-of-the-art approaches for faithfulness when evaluated automatically and by humans.

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Focus Attention: Promoting Faithfulness and Diversity in Summarization
Rahul Aralikatte | Shashi Narayan | Joshua Maynez | Sascha Rothe | Ryan McDonald
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)

Professional summaries are written with document-level information, such as the theme of the document, in mind. This is in contrast with most seq2seq decoders which simultaneously learn to focus on salient content, while deciding what to generate, at each decoding step. With the motivation to narrow this gap, we introduce Focus Attention Mechanism, a simple yet effective method to encourage decoders to proactively generate tokens that are similar or topical to the input document. Further, we propose a Focus Sampling method to enable generation of diverse summaries, an area currently understudied in summarization. When evaluated on the BBC extreme summarization task, two state-of-the-art models augmented with Focus Attention generate summaries that are closer to the target and more faithful to their input documents, outperforming their vanilla counterparts on ROUGE and multiple faithfulness measures. We also empirically demonstrate that Focus Sampling is more effective in generating diverse and faithful summaries than top-k or nucleus sampling-based decoding methods.

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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann | Tosin Adewumi | Karmanya Aggarwal | Pawan Sasanka Ammanamanchi | Anuoluwapo Aremu | Antoine Bosselut | Khyathi Raghavi Chandu | Miruna-Adriana Clinciu | Dipanjan Das | Kaustubh Dhole | Wanyu Du | Esin Durmus | Ondřej Dušek | Chris Chinenye Emezue | Varun Gangal | Cristina Garbacea | Tatsunori Hashimoto | Yufang Hou | Yacine Jernite | Harsh Jhamtani | Yangfeng Ji | Shailza Jolly | Mihir Kale | Dhruv Kumar | Faisal Ladhak | Aman Madaan | Mounica Maddela | Khyati Mahajan | Saad Mahamood | Bodhisattwa Prasad Majumder | Pedro Henrique Martins | Angelina McMillan-Major | Simon Mille | Emiel van Miltenburg | Moin Nadeem | Shashi Narayan | Vitaly Nikolaev | Andre Niyongabo Rubungo | Salomey Osei | Ankur Parikh | Laura Perez-Beltrachini | Niranjan Ramesh Rao | Vikas Raunak | Juan Diego Rodriguez | Sashank Santhanam | João Sedoc | Thibault Sellam | Samira Shaikh | Anastasia Shimorina | Marco Antonio Sobrevilla Cabezudo | Hendrik Strobelt | Nishant Subramani | Wei Xu | Diyi Yang | Akhila Yerukola | Jiawei Zhou
Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)

We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for the 2021 shared task at the associated GEM Workshop.

2020

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On Faithfulness and Factuality in Abstractive Summarization
Joshua Maynez | Shashi Narayan | Bernd Bohnet | Ryan McDonald
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

It is well known that the standard likelihood training and approximate decoding objectives in neural text generation models lead to less human-like responses for open-ended tasks such as language modeling and story generation. In this paper we have analyzed limitations of these models for abstractive document summarization and found that these models are highly prone to hallucinate content that is unfaithful to the input document. We conducted a large scale human evaluation of several neural abstractive summarization systems to better understand the types of hallucinations they produce. Our human annotators found substantial amounts of hallucinated content in all model generated summaries. However, our analysis does show that pretrained models are better summarizers not only in terms of raw metrics, i.e., ROUGE, but also in generating faithful and factual summaries as evaluated by humans. Furthermore, we show that textual entailment measures better correlate with faithfulness than standard metrics, potentially leading the way to automatic evaluation metrics as well as training and decoding criteria.

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Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
Sascha Rothe | Shashi Narayan | Aliaksei Severyn
Transactions of the Association for Computational Linguistics, Volume 8

Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT, GPT-2, and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation, Text Summarization, Sentence Splitting, and Sentence Fusion.

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Stepwise Extractive Summarization and Planning with Structured Transformers
Shashi Narayan | Joshua Maynez | Jakub Adamek | Daniele Pighin | Blaz Bratanic | Ryan McDonald
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We propose encoder-centric stepwise models for extractive summarization using structured transformers – HiBERT and Extended Transformers. We enable stepwise summarization by injecting the previously generated summary into the structured transformer as an auxiliary sub-structure. Our models are not only efficient in modeling the structure of long inputs, but they also do not rely on task-specific redundancy-aware modeling, making them a general purpose extractive content planner for different tasks. When evaluated on CNN/DailyMail extractive summarization, stepwise models achieve state-of-the-art performance in terms of Rouge without any redundancy aware modeling or sentence filtering. This also holds true for Rotowire table-to-text generation, where our models surpass previously reported metrics for content selection, planning and ordering, highlighting the strength of stepwise modeling. Amongst the two structured transformers we test, stepwise Extended Transformers provides the best performance across both datasets and sets a new standard for these challenges.

2019

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Jointly Extracting and Compressing Documents with Summary State Representations
Afonso Mendes | Shashi Narayan | Sebastião Miranda | Zita Marinho | André F. T. Martins | Shay B. Cohen
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)

We present a new neural model for text summarization that first extracts sentences from a document and then compresses them. The pro-posed model offers a balance that sidesteps thedifficulties in abstractive methods while gener-ating more concise summaries than extractivemethods. In addition, our model dynamically determines the length of the output summary based on the gold summaries it observes during training and does not require length constraints typical to extractive summarization. The model achieves state-of-the-art results on the CNN/DailyMail and Newsroom datasets, improving over current extractive and abstractive methods. Human evaluations demonstratethat our model generates concise and informa-tive summaries. We also make available a new dataset of oracle compressive summaries derived automatically from the CNN/DailyMailreference summaries.

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HighRES: Highlight-based Reference-less Evaluation of Summarization
Hardy Hardy | Shashi Narayan | Andreas Vlachos
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

There has been substantial progress in summarization research enabled by the availability of novel, often large-scale, datasets and recent advances on neural network-based approaches. However, manual evaluation of the system generated summaries is inconsistent due to the difficulty the task poses to human non-expert readers. To address this issue, we propose a novel approach for manual evaluation, Highlight-based Reference-less Evaluation of Summarization (HighRES), in which summaries are assessed by multiple annotators against the source document via manually highlighted salient content in the latter. Thus summary assessment on the source document by human judges is facilitated, while the highlights can be used for evaluating multiple systems. To validate our approach we employ crowd-workers to augment with highlights a recently proposed dataset and compare two state-of-the-art systems. We demonstrate that HighRES improves inter-annotator agreement in comparison to using the source document directly, while they help emphasize differences among systems that would be ignored under other evaluation approaches.

2018

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Privacy-preserving Neural Representations of Text
Maximin Coavoux | Shashi Narayan | Shay B. Cohen
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

This article deals with adversarial attacks towards deep learning systems for Natural Language Processing (NLP), in the context of privacy protection. We study a specific type of attack: an attacker eavesdrops on the hidden representations of a neural text classifier and tries to recover information about the input text. Such scenario may arise in situations when the computation of a neural network is shared across multiple devices, e.g. some hidden representation is computed by a user’s device and sent to a cloud-based model. We measure the privacy of a hidden representation by the ability of an attacker to predict accurately specific private information from it and characterize the tradeoff between the privacy and the utility of neural representations. Finally, we propose several defense methods based on modified training objectives and show that they improve the privacy of neural representations.

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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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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.

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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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Deep Learning Approaches to Text Production
Claire Gardent | Shashi Narayan
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts

Text production is a key component of many NLP applications. In data-driven approaches, it is used for instance, to generate dialogue turns from dialogue moves, to verbalise the content of Knowledge bases or to generate natural English sentences from rich linguistic representations, such as dependency trees or Abstract Meaning Representations. In text-driven methods on the other hand, text production is at work in sentence compression, sentence fusion, paraphrasing, sentence (or text) simplification, text summarisation and end-to-end dialogue systems. Following the success of encoder-decoder models in modeling sequence-rewriting tasks such as machine translation, deep learning models have successfully been applied to the various text production tasks. In this tutorial, we will cover the fundamentals and the state-of-the-art research on neural models for text production. Each text production task raises a slightly different communication goal (e.g, how to take the dialogue context into account when producing a dialogue turn; how to detect and merge relevant information when summarising a text; or how to produce a well-formed text that correctly capture the information contained in some input data in the case of data-to-text generation). We will outline the constraints specific to each subtasks and examine how the existing neural models account for them.

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Local String Transduction as Sequence Labeling
Joana Ribeiro | Shashi Narayan | Shay B. Cohen | Xavier Carreras
Proceedings of the 27th International Conference on Computational Linguistics

We show that the general problem of string transduction can be reduced to the problem of sequence labeling. While character deletion and insertions are allowed in string transduction, they do not exist in sequence labeling. We show how to overcome this difference. Our approach can be used with any sequence labeling algorithm and it works best for problems in which string transduction imposes a strong notion of locality (no long range dependencies). We experiment with spelling correction for social media, OCR correction, and morphological inflection, and we see that it behaves better than seq2seq models and yields state-of-the-art results in several cases.

2017

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The SUMMA Platform Prototype
Renars Liepins | Ulrich Germann | Guntis Barzdins | Alexandra Birch | Steve Renals | Susanne Weber | Peggy van der Kreeft | Hervé Bourlard | João Prieto | Ondřej Klejch | Peter Bell | Alexandros Lazaridis | Alfonso Mendes | Sebastian Riedel | Mariana S. C. Almeida | Pedro Balage | Shay B. Cohen | Tomasz Dwojak | Philip N. Garner | Andreas Giefer | Marcin Junczys-Dowmunt | Hina Imran | David Nogueira | Ahmed Ali | Sebastião Miranda | Andrei Popescu-Belis | Lesly Miculicich Werlen | Nikos Papasarantopoulos | Abiola Obamuyide | Clive Jones | Fahim Dalvi | Andreas Vlachos | Yang Wang | Sibo Tong | Rico Sennrich | Nikolaos Pappas | Shashi Narayan | Marco Damonte | Nadir Durrani | Sameer Khurana | Ahmed Abdelali | Hassan Sajjad | Stephan Vogel | David Sheppey | Chris Hernon | Jeff Mitchell
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams.

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The WebNLG Challenge: Generating Text from RDF Data
Claire Gardent | Anastasia Shimorina | Shashi Narayan | Laura Perez-Beltrachini
Proceedings of the 10th International Conference on Natural Language Generation

The WebNLG challenge consists in mapping sets of RDF triples to text. It provides a common benchmark on which to train, evaluate and compare “microplanners”, i.e. generation systems that verbalise a given content by making a range of complex interacting choices including referring expression generation, aggregation, lexicalisation, surface realisation and sentence segmentation. In this paper, we introduce the microplanning task, describe data preparation, introduce our evaluation methodology, analyse participant results and provide a brief description of the participating systems.

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Creating Training Corpora for NLG Micro-Planners
Claire Gardent | Anastasia Shimorina | Shashi Narayan | Laura Perez-Beltrachini
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper, we present a novel framework for semi-automatically creating linguistically challenging micro-planning data-to-text corpora from existing Knowledge Bases. Because our method pairs data of varying size and shape with texts ranging from simple clauses to short texts, a dataset created using this framework provides a challenging benchmark for microplanning. Another feature of this framework is that it can be applied to any large scale knowledge base and can therefore be used to train and learn KB verbalisers. We apply our framework to DBpedia data and compare the resulting dataset with Wen et al. 2016’s. We show that while Wen et al.’s dataset is more than twice larger than ours, it is less diverse both in terms of input and in terms of text. We thus propose our corpus generation framework as a novel method for creating challenging data sets from which NLG models can be learned which are capable of handling the complex interactions occurring during in micro-planning between lexicalisation, aggregation, surface realisation, referring expression generation and sentence segmentation. To encourage researchers to take up this challenge, we made available a dataset of 21,855 data/text pairs created using this framework in the context of the WebNLG shared task.

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Split and Rephrase
Shashi Narayan | Claire Gardent | Shay B. Cohen | Anastasia Shimorina
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We propose a new sentence simplification task (Split-and-Rephrase) where the aim is to split a complex sentence into a meaning preserving sequence of shorter sentences. Like sentence simplification, splitting-and-rephrasing has the potential of benefiting both natural language processing and societal applications. Because shorter sentences are generally better processed by NLP systems, it could be used as a preprocessing step which facilitates and improves the performance of parsers, semantic role labellers and machine translation systems. It should also be of use for people with reading disabilities because it allows the conversion of longer sentences into shorter ones. This paper makes two contributions towards this new task. First, we create and make available a benchmark consisting of 1,066,115 tuples mapping a single complex sentence to a sequence of sentences expressing the same meaning. Second, we propose five models (vanilla sequence-to-sequence to semantically-motivated models) to understand the difficulty of the proposed task.

2016

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Optimizing Spectral Learning for Parsing
Shashi Narayan | Shay B. Cohen
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Encoding Prior Knowledge with Eigenword Embeddings
Dominique Osborne | Shashi Narayan | Shay B. Cohen
Transactions of the Association for Computational Linguistics, Volume 4

Canonical correlation analysis (CCA) is a method for reducing the dimension of data represented using two views. It has been previously used to derive word embeddings, where one view indicates a word, and the other view indicates its context. We describe a way to incorporate prior knowledge into CCA, give a theoretical justification for it, and test it by deriving word embeddings and evaluating them on a myriad of datasets.

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Unsupervised Sentence Simplification Using Deep Semantics
Shashi Narayan | Claire Gardent
Proceedings of the 9th International Natural Language Generation conference

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Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing
Shashi Narayan | Siva Reddy | Shay B. Cohen
Proceedings of the 9th International Natural Language Generation conference

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The WebNLG Challenge: Generating Text from DBPedia Data
Emilie Colin | Claire Gardent | Yassine M’rabet | Shashi Narayan | Laura Perez-Beltrachini
Proceedings of the 9th International Natural Language Generation conference

2015

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Diversity in Spectral Learning for Natural Language Parsing
Shashi Narayan | Shay B. Cohen
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Multiple Adjunction in Feature-Based Tree-Adjoining Grammar
Claire Gardent | Shashi Narayan
Computational Linguistics, Volume 41, Issue 1 - March 2015

2014

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Hybrid Simplification using Deep Semantics and Machine Translation
Shashi Narayan | Claire Gardent
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Generating Elliptic Coordination
Claire Gardent | Shashi Narayan
Proceedings of the 14th European Workshop on Natural Language Generation

2012

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Error Mining on Dependency Trees
Claire Gardent | Shashi Narayan
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Error Mining with Suspicion Trees: Seeing the Forest for the Trees
Shashi Narayan | Claire Gardent
Proceedings of COLING 2012

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Structure-Driven Lexicalist Generation
Shashi Narayan | Claire Gardent
Proceedings of COLING 2012

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