Roi Reichart


2021

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Proceedings of the Third Workshop on Computational Typology and Multilingual NLP
Ekaterina Vylomova | Elizabeth Salesky | Sabrina Mielke | Gabriella Lapesa | Ritesh Kumar | Harald Hammarström | Ivan Vulić | Anna Korhonen | Roi Reichart | Edoardo Maria Ponti | Ryan Cotterell
Proceedings of the Third Workshop on Computational Typology and Multilingual NLP

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Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification
Yi Zhu | Ehsan Shareghi | Yingzhen Li | Roi Reichart | Anna Korhonen
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Semi-supervised learning through deep generative models and multi-lingual pretraining techniques have orchestrated tremendous success across different areas of NLP. Nonetheless, their development has happened in isolation, while the combination of both could potentially be effective for tackling task-specific labelled data shortage. To bridge this gap, we combine semi-supervised deep generative models and multi-lingual pretraining to form a pipeline for document classification task. Compared to strong supervised learning baselines, our semi-supervised classification framework is highly competitive and outperforms the state-of-the-art counterparts in low-resource settings across several languages.

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A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters
Mengjie Zhao | Yi Zhu | Ehsan Shareghi | Ivan Vulić | Roi Reichart | Anna Korhonen | Hinrich Schütze
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)

Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT. Despite its growing popularity, little to no attention has been paid to standardizing and analyzing the design of few-shot experiments. In this work, we highlight a fundamental risk posed by this shortcoming, illustrating that the model exhibits a high degree of sensitivity to the selection of few shots. We conduct a large-scale experimental study on 40 sets of sampled few shots for six diverse NLP tasks across up to 40 languages. We provide an analysis of success and failure cases of few-shot transfer, which highlights the role of lexical features. Additionally, we show that a straightforward full model finetuning approach is quite effective for few-shot transfer, outperforming several state-of-the-art few-shot approaches. As a step towards standardizing few-shot crosslingual experimental designs, we make our sampled few shots publicly available.

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Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions
Daniel Rosenberg | Itai Gat | Amir Feder | Roi Reichart
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Deep learning algorithms have shown promising results in visual question answering (VQA) tasks, but a more careful look reveals that they often do not understand the rich signal they are being fed with. To understand and better measure the generalization capabilities of VQA systems, we look at their robustness to counterfactually augmented data. Our proposed augmentations are designed to make a focused intervention on a specific property of the question such that the answer changes. Using these augmentations, we propose a new robustness measure, Robustness to Augmented Data (RAD), which measures the consistency of model predictions between original and augmented examples. Through extensive experimentation, we show that RAD, unlike classical accuracy measures, can quantify when state-of-the-art systems are not robust to counterfactuals. We find substantial failure cases which reveal that current VQA systems are still brittle. Finally, we connect between robustness and generalization, demonstrating the predictive power of RAD for performance on unseen augmentations.

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Proceedings of the Second Workshop on Domain Adaptation for NLP
Eyal Ben-David | Shay Cohen | Ryan McDonald | Barbara Plank | Roi Reichart | Guy Rotman | Yftah Ziser
Proceedings of the Second Workshop on Domain Adaptation for NLP

2020

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Semantically Driven Sentence Fusion: Modeling and Evaluation
Eyal Ben-David | Orgad Keller | Eric Malmi | Idan Szpektor | Roi Reichart
Findings of the Association for Computational Linguistics: EMNLP 2020

Sentence fusion is the task of joining related sentences into coherent text. Current training and evaluation schemes for this task are based on single reference ground-truths and do not account for valid fusion variants. We show that this hinders models from robustly capturing the semantic relationship between input sentences. To alleviate this, we present an approach in which ground-truth solutions are automatically expanded into multiple references via curated equivalence classes of connective phrases. We apply this method to a large-scale dataset and use the augmented dataset for both model training and evaluation. To improve the learning of semantic representation using multiple references, we enrich the model with auxiliary discourse classification tasks under a multi-tasking framework. Our experiments highlight the improvements of our approach over state-of-the-art models.

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Proceedings of the Second Workshop on Computational Research in Linguistic Typology
Ekaterina Vylomova | Edoardo M. Ponti | Eitan Grossman | Arya D. McCarthy | Yevgeni Berzak | Haim Dubossarsky | Ivan Vulić | Roi Reichart | Anna Korhonen | Ryan Cotterell
Proceedings of the Second Workshop on Computational Research in Linguistic Typology

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PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models
Eyal Ben-David | Carmel Rabinovitz | Roi Reichart
Transactions of the Association for Computational Linguistics, Volume 8

Pivot-based neural representation models have led to significant progress in domain adaptation for NLP. However, previous research following this approach utilize only labeled data from the source domain and unlabeled data from the source and target domains, but neglect to incorporate massive unlabeled corpora that are not necessarily drawn from these domains. To alleviate this, we propose PERL: A representation learning model that extends contextualized word embedding models such as BERT (Devlin et al., 2019) with pivot-based fine-tuning. PERL outperforms strong baselines across 22 sentiment classification domain adaptation setups, improves in-domain model performance, yields effective reduced-size models, and increases model stability.1

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Multidirectional Associative Optimization of Function-Specific Word Representations
Daniela Gerz | Ivan Vulić | Marek Rei | Roi Reichart | Anna Korhonen
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We present a neural framework for learning associations between interrelated groups of words such as the ones found in Subject-Verb-Object (SVO) structures. Our model induces a joint function-specific word vector space, where vectors of e.g. plausible SVO compositions lie close together. The model retains information about word group membership even in the joint space, and can thereby effectively be applied to a number of tasks reasoning over the SVO structure. We show the robustness and versatility of the proposed framework by reporting state-of-the-art results on the tasks of estimating selectional preference and event similarity. The results indicate that the combinations of representations learned with our task-independent model outperform task-specific architectures from prior work, while reducing the number of parameters by up to 95%.

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Predicting In-Game Actions from Interviews of NBA Players
Nadav Oved | Amir Feder | Roi Reichart
Computational Linguistics, Volume 46, Issue 3 - September 2020

Sports competitions are widely researched in computer and social science, with the goal of understanding how players act under uncertainty. Although there is an abundance of computational work on player metrics prediction based on past performance, very few attempts to incorporate out-of-game signals have been made. Specifically, it was previously unclear whether linguistic signals gathered from players’ interviews can add information that does not appear in performance metrics. To bridge that gap, we define text classification tasks of predicting deviations from mean in NBA players’ in-game actions, which are associated with strategic choices, player behavior, and risk, using their choice of language prior to the game. We collected a data set of transcripts from key NBA players’ pre-game interviews and their in-game performance metrics, totalling 5,226 interview-metric pairs. We design neural models for players’ action prediction based on increasingly more complex aspects of the language signals in their open-ended interviews. Our models can make their predictions based on the textual signal alone, or on a combination of that signal with signals from past-performance metrics. Our text-based models outperform strong baselines trained on performance metrics only, demonstrating the importance of language usage for action prediction. Moreover, the models that utilize both textual input and past-performance metrics produced the best results. Finally, as neural networks are notoriously difficult to interpret, we propose a method for gaining further insight into what our models have learned. Particularly, we present a latent Dirichlet allocation–based analysis, where we interpret model predictions in terms of correlated topics. We find that our best performing textual model is most associated with topics that are intuitively related to each prediction task and that better models yield higher correlation with more informative topics.1

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Multi-SimLex: A Large-Scale Evaluation of Multilingual and Crosslingual Lexical Semantic Similarity
Ivan Vulić | Simon Baker | Edoardo Maria Ponti | Ulla Petti | Ira Leviant | Kelly Wing | Olga Majewska | Eden Bar | Matt Malone | Thierry Poibeau | Roi Reichart | Anna Korhonen
Computational Linguistics, Volume 46, Issue 4 - December 2020

We introduce Multi-SimLex, a large-scale lexical resource and evaluation benchmark covering data sets for 12 typologically diverse languages, including major languages (e.g., Mandarin Chinese, Spanish, Russian) as well as less-resourced ones (e.g., Welsh, Kiswahili). Each language data set is annotated for the lexical relation of semantic similarity and contains 1,888 semantically aligned concept pairs, providing a representative coverage of word classes (nouns, verbs, adjectives, adverbs), frequency ranks, similarity intervals, lexical fields, and concreteness levels. Additionally, owing to the alignment of concepts across languages, we provide a suite of 66 crosslingual semantic similarity data sets. Because of its extensive size and language coverage, Multi-SimLex provides entirely novel opportunities for experimental evaluation and analysis. On its monolingual and crosslingual benchmarks, we evaluate and analyze a wide array of recent state-of-the-art monolingual and crosslingual representation models, including static and contextualized word embeddings (such as fastText, monolingual and multilingual BERT, XLM), externally informed lexical representations, as well as fully unsupervised and (weakly) supervised crosslingual word embeddings. We also present a step-by-step data set creation protocol for creating consistent, Multi-Simlex–style resources for additional languages. We make these contributions—the public release of Multi-SimLex data sets, their creation protocol, strong baseline results, and in-depth analyses which can be helpful in guiding future developments in multilingual lexical semantics and representation learning—available via a Web site that will encourage community effort in further expansion of Multi-Simlex to many more languages. Such a large-scale semantic resource could inspire significant further advances in NLP across languages.

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The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures
Haim Dubossarsky | Ivan Vulić | Roi Reichart | Anna Korhonen
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Performance in cross-lingual NLP tasks is impacted by the (dis)similarity of languages at hand: e.g., previous work has suggested there is a connection between the expected success of bilingual lexicon induction (BLI) and the assumption of (approximate) isomorphism between monolingual embedding spaces. In this work we present a large-scale study focused on the correlations between monolingual embedding space similarity and task performance, covering thousands of language pairs and four different tasks: BLI, parsing, POS tagging and MT. We hypothesize that statistics of the spectrum of each monolingual embedding space indicate how well they can be aligned. We then introduce several isomorphism measures between two embedding spaces, based on the relevant statistics of their individual spectra. We empirically show that (1) language similarity scores derived from such spectral isomorphism measures are strongly associated with performance observed in different cross-lingual tasks, and (2) our spectral-based measures consistently outperform previous standard isomorphism measures, while being computationally more tractable and easier to interpret. Finally, our measures capture complementary information to typologically driven language distance measures, and the combination of measures from the two families yields even higher task performance correlations.

2019

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Cross-lingual Semantic Specialization via Lexical Relation Induction
Edoardo Maria Ponti | Ivan Vulić | Goran Glavaš | Roi Reichart | Anna Korhonen
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 specialization integrates structured linguistic knowledge from external resources (such as lexical relations in WordNet) into pretrained distributional vectors in the form of constraints. However, this technique cannot be leveraged in many languages, because their structured external resources are typically incomplete or non-existent. To bridge this gap, we propose a novel method that transfers specialization from a resource-rich source language (English) to virtually any target language. Our specialization transfer comprises two crucial steps: 1) Inducing noisy constraints in the target language through automatic word translation; and 2) Filtering the noisy constraints via a state-of-the-art relation prediction model trained on the source language constraints. This allows us to specialize any set of distributional vectors in the target language with the refined constraints. We prove the effectiveness of our method through intrinsic word similarity evaluation in 8 languages, and with 3 downstream tasks in 5 languages: lexical simplification, dialog state tracking, and semantic textual similarity. The gains over the previous state-of-art specialization methods are substantial and consistent across languages. Our results also suggest that the transfer method is effective even for lexically distant source-target language pairs. Finally, as a by-product, our method produces lists of WordNet-style lexical relations in resource-poor languages.

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Towards Zero-shot Language Modeling
Edoardo Maria Ponti | Ivan Vulić | Ryan Cotterell | Roi Reichart | Anna Korhonen
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Can we construct a neural language model which is inductively biased towards learning human language? Motivated by this question, we aim at constructing an informative prior for held-out languages on the task of character-level, open-vocabulary language modelling. We obtain this prior as the posterior over network weights conditioned on the data from a sample of training languages, which is approximated through Laplace’s method. Based on a large and diverse sample of languages, the use of our prior outperforms baseline models with an uninformative prior in both zero-shot and few-shot settings, showing that the prior is imbued with universal linguistic knowledge. Moreover, we harness broad language-specific information available for most languages of the world, i.e., features from typological databases, as distant supervision for held-out languages. We explore several language modelling conditioning techniques, including concatenation and meta-networks for parameter generation. They appear beneficial in the few-shot setting, but ineffective in the zero-shot setting. Since the paucity of even plain digital text affects the majority of the world’s languages, we hope that these insights will broaden the scope of applications for language technology.

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Do We Really Need Fully Unsupervised Cross-Lingual Embeddings?
Ivan Vulić | Goran Glavaš | Roi Reichart | Anna Korhonen
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Recent efforts in cross-lingual word embedding (CLWE) learning have predominantly focused on fully unsupervised approaches that project monolingual embeddings into a shared cross-lingual space without any cross-lingual signal. The lack of any supervision makes such approaches conceptually attractive. Yet, their only core difference from (weakly) supervised projection-based CLWE methods is in the way they obtain a seed dictionary used to initialize an iterative self-learning procedure. The fully unsupervised methods have arguably become more robust, and their primary use case is CLWE induction for pairs of resource-poor and distant languages. In this paper, we question the ability of even the most robust unsupervised CLWE approaches to induce meaningful CLWEs in these more challenging settings. A series of bilingual lexicon induction (BLI) experiments with 15 diverse languages (210 language pairs) show that fully unsupervised CLWE methods still fail for a large number of language pairs (e.g., they yield zero BLI performance for 87/210 pairs). Even when they succeed, they never surpass the performance of weakly supervised methods (seeded with 500-1,000 translation pairs) using the same self-learning procedure in any BLI setup, and the gaps are often substantial. These findings call for revisiting the main motivations behind fully unsupervised CLWE methods.

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Bayesian Learning for Neural Dependency Parsing
Ehsan Shareghi | Yingzhen Li | Yi Zhu | Roi Reichart | Anna Korhonen
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)

While neural dependency parsers provide state-of-the-art accuracy for several languages, they still rely on large amounts of costly labeled training data. We demonstrate that in the small data regime, where uncertainty around parameter estimation and model prediction matters the most, Bayesian neural modeling is very effective. In order to overcome the computational and statistical costs of the approximate inference step in this framework, we utilize an efficient sampling procedure via stochastic gradient Langevin dynamics to generate samples from the approximated posterior. Moreover, we show that our Bayesian neural parser can be further improved when integrated into a multi-task parsing and POS tagging framework, designed to minimize task interference via an adversarial procedure. When trained and tested on 6 languages with less than 5k training instances, our parser consistently outperforms the strong bilstm baseline (Kiperwasser and Goldberg, 2016). Compared with the biaffine parser (Dozat et al., 2017) our model achieves an improvement of up to 3% for Vietnames and Irish, while our multi-task model achieves an improvement of up to 9% across five languages: Farsi, Russian, Turkish, Vietnamese, and Irish.

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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
Edoardo Maria Ponti | Helen O’Horan | Yevgeni Berzak | Ivan Vulić | Roi Reichart | Thierry Poibeau | Ekaterina Shutova | Anna Korhonen
Computational Linguistics, Volume 45, Issue 3 - September 2019

Linguistic typology aims to capture structural and semantic variation across the world’s languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-utilization of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such an approach could be facilitated by recent developments in data-driven induction of typological knowledge.

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On the Importance of Subword Information for Morphological Tasks in Truly Low-Resource Languages
Yi Zhu | Benjamin Heinzerling | Ivan Vulić | Michael Strube | Roi Reichart | Anna Korhonen
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Recent work has validated the importance of subword information for word representation learning. Since subwords increase parameter sharing ability in neural models, their value should be even more pronounced in low-data regimes. In this work, we therefore provide a comprehensive analysis focused on the usefulness of subwords for word representation learning in truly low-resource scenarios and for three representative morphological tasks: fine-grained entity typing, morphological tagging, and named entity recognition. We conduct a systematic study that spans several dimensions of comparison: 1) type of data scarcity which can stem from the lack of task-specific training data, or even from the lack of unannotated data required to train word embeddings, or both; 2) language type by working with a sample of 16 typologically diverse languages including some truly low-resource ones (e.g. Rusyn, Buryat, and Zulu); 3) the choice of the subword-informed word representation method. Our main results show that subword-informed models are universally useful across all language types, with large gains over subword-agnostic embeddings. They also suggest that the effective use of subwords largely depends on the language (type) and the task at hand, as well as on the amount of available data for training the embeddings and task-based models, where having sufficient in-task data is a more critical requirement.

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Proceedings of TyP-NLP: The First Workshop on Typology for Polyglot NLP
Haim Dubossarsky | Arya D. McCarthy | Edoardo Maria Ponti | Ivan Vulić | Ekaterina Vylomova | Yevgeni Berzak | Ryan Cotterell | Manaal Faruqui | Anna Korhonen | Roi Reichart
Proceedings of TyP-NLP: The First Workshop on Typology for Polyglot NLP

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Deep Dominance - How to Properly Compare Deep Neural Models
Rotem Dror | Segev Shlomov | Roi Reichart
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Comparing between Deep Neural Network (DNN) models based on their performance on unseen data is crucial for the progress of the NLP field. However, these models have a large number of hyper-parameters and, being non-convex, their convergence point depends on the random values chosen at initialization and during training. Proper DNN comparison hence requires a comparison between their empirical score distributions on unseen data, rather than between single evaluation scores as is standard for more simple, convex models. In this paper, we propose to adapt to this problem a recently proposed test for the Almost Stochastic Dominance relation between two distributions. We define the criteria for a high quality comparison method between DNNs, and show, both theoretically and through analysis of extensive experimental results with leading DNN models for sequence tagging tasks, that the proposed test meets all criteria while previously proposed methods fail to do so. We hope the test we propose here will set a new working practice in the NLP community.

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Zero-Shot Semantic Parsing for Instructions
Ofer Givoli | Roi Reichart
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We consider a zero-shot semantic parsing task: parsing instructions into compositional logical forms, in domains that were not seen during training. We present a new dataset with 1,390 examples from 7 application domains (e.g. a calendar or a file manager), each example consisting of a triplet: (a) the application’s initial state, (b) an instruction, to be carried out in the context of that state, and (c) the state of the application after carrying out the instruction. We introduce a new training algorithm that aims to train a semantic parser on examples from a set of source domains, so that it can effectively parse instructions from an unknown target domain. We integrate our algorithm into the floating parser of Pasupat and Liang (2015), and further augment the parser with features and a logical form candidate filtering logic, to support zero-shot adaptation. Our experiments with various zero-shot adaptation setups demonstrate substantial performance gains over a non-adapted parser.

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Task Refinement Learning for Improved Accuracy and Stability of Unsupervised Domain Adaptation
Yftah Ziser | Roi Reichart
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Pivot Based Language Modeling (PBLM) (Ziser and Reichart, 2018a), combining LSTMs with pivot-based methods, has yielded significant progress in unsupervised domain adaptation. However, this approach is still challenged by the large pivot detection problem that should be solved, and by the inherent instability of LSTMs. In this paper we propose a Task Refinement Learning (TRL) approach, in order to solve these problems. Our algorithms iteratively train the PBLM model, gradually increasing the information exposed about each pivot. TRL-PBLM achieves stateof- the-art accuracy in six domain adaptation setups for sentiment classification. Moreover, it is much more stable than plain PBLM across model configurations, making the model much better fitted for practical use.

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Perturbation Based Learning for Structured NLP Tasks with Application to Dependency Parsing
Amichay Doitch | Ram Yazdi | Tamir Hazan | Roi Reichart
Transactions of the Association for Computational Linguistics, Volume 7

The best solution of structured prediction models in NLP is often inaccurate because of limited expressive power of the model or to non-exact parameter estimation. One way to mitigate this problem is sampling candidate solutions from the model’s solution space, reasoning that effective exploration of this space should yield high-quality solutions. Unfortunately, sampling is often computationally hard and many works hence back-off to sub-optimal strategies, such as extraction of the best scoring solutions of the model, which are not as diverse as sampled solutions. In this paper we propose a perturbation-based approach where sampling from a probabilistic model is computationally efficient. We present a learning algorithm for the variance of the perturbations, and empirically demonstrate its importance. Moreover, while finding the argmax in our model is intractable, we propose an efficient and effective approximation. We apply our framework to cross-lingual dependency parsing across 72 corpora from 42 languages and to lightly supervised dependency parsing across 13 corpora from 12 languages, and demonstrate strong results in terms of both the quality of the entire solution list and of the final solution.1

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Deep Contextualized Self-training for Low Resource Dependency Parsing
Guy Rotman | Roi Reichart
Transactions of the Association for Computational Linguistics, Volume 7

Neural dependency parsing has proven very effective, achieving state-of-the-art results on numerous domains and languages. Unfortunately, it requires large amounts of labeled data, which is costly and laborious to create. In this paper we propose a self-training algorithm that alleviates this annotation bottleneck by training a parser on its own output. Our Deep Contextualized Self-training (DCST) algorithm utilizes representation models trained on sequence labeling tasks that are derived from the parser’s output when applied to unlabeled data, and integrates these models with the base parser through a gating mechanism. We conduct experiments across multiple languages, both in low resource in-domain and in cross-domain setups, and demonstrate that DCST substantially outperforms traditional self-training as well as recent semi-supervised training methods.1

2018

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Pivot Based Language Modeling for Improved Neural Domain Adaptation
Yftah Ziser | Roi Reichart
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Representation learning with pivot-based methods and with Neural Networks (NNs) have lead to significant progress in domain adaptation for Natural Language Processing. However, most previous work that follows these approaches does not explicitly exploit the structure of the input text, and its output is most often a single representation vector for the entire text. In this paper we present the Pivot Based Language Model (PBLM), a representation learning model that marries together pivot-based and NN modeling in a structure aware manner. Particularly, our model processes the information in the text with a sequential NN (LSTM) and its output consists of a representation vector for every input word. Unlike most previous representation learning models in domain adaptation, PBLM can naturally feed structure aware text classifiers such as LSTM and CNN. We experiment with the task of cross-domain sentiment classification on 20 domain pairs and show substantial improvements over strong baselines.

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Bridging Languages through Images with Deep Partial Canonical Correlation Analysis
Guy Rotman | Ivan Vulić | Roi Reichart
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present a deep neural network that leverages images to improve bilingual text embeddings. Relying on bilingual image tags and descriptions, our approach conditions text embedding induction on the shared visual information for both languages, producing highly correlated bilingual embeddings. In particular, we propose a novel model based on Partial Canonical Correlation Analysis (PCCA). While the original PCCA finds linear projections of two views in order to maximize their canonical correlation conditioned on a shared third variable, we introduce a non-linear Deep PCCA (DPCCA) model, and develop a new stochastic iterative algorithm for its optimization. We evaluate PCCA and DPCCA on multilingual word similarity and cross-lingual image description retrieval. Our models outperform a large variety of previous methods, despite not having access to any visual signal during test time inference.

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The Hitchhiker’s Guide to Testing Statistical Significance in Natural Language Processing
Rotem Dror | Gili Baumer | Segev Shlomov | Roi Reichart
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Statistical significance testing is a standard statistical tool designed to ensure that experimental results are not coincidental. In this opinion/ theoretical paper we discuss the role of statistical significance testing in Natural Language Processing (NLP) research. We establish the fundamental concepts of significance testing and discuss the specific aspects of NLP tasks, experimental setups and evaluation measures that affect the choice of significance tests in NLP research. Based on this discussion we propose a simple practical protocol for statistical significance test selection in NLP setups and accompany this protocol with a brief survey of the most relevant tests. We then survey recent empirical papers published in ACL and TACL during 2017 and show that while our community assigns great value to experimental results, statistical significance testing is often ignored or misused. We conclude with a brief discussion of open issues that should be properly addressed so that this important tool can be applied. in NLP research in a statistically sound manner.

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Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP
Edoardo Maria Ponti | Roi Reichart | Anna Korhonen | Ivan Vulić
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The transfer or share of knowledge between languages is a potential solution to resource scarcity in NLP. However, the effectiveness of cross-lingual transfer can be challenged by variation in syntactic structures. Frameworks such as Universal Dependencies (UD) are designed to be cross-lingually consistent, but even in carefully designed resources trees representing equivalent sentences may not always overlap. In this paper, we measure cross-lingual syntactic variation, or anisomorphism, in the UD treebank collection, considering both morphological and structural properties. We show that reducing the level of anisomorphism yields consistent gains in cross-lingual transfer tasks. We introduce a source language selection procedure that facilitates effective cross-lingual parser transfer, and propose a typologically driven method for syntactic tree processing which reduces anisomorphism. Our results show the effectiveness of this method for both machine translation and cross-lingual sentence similarity, demonstrating the importance of syntactic structure compatibility for boosting cross-lingual transfer in NLP.

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Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction
Daniela Gerz | Ivan Vulić | Edoardo Ponti | Jason Naradowsky | Roi Reichart | Anna Korhonen
Transactions of the Association for Computational Linguistics, Volume 6

Neural architectures are prominent in the construction of language models (LMs). However, word-level prediction is typically agnostic of subword-level information (characters and character sequences) and operates over a closed vocabulary, consisting of a limited word set. Indeed, while subword-aware models boost performance across a variety of NLP tasks, previous work did not evaluate the ability of these models to assist next-word prediction in language modeling tasks. Such subword-level informed models should be particularly effective for morphologically-rich languages (MRLs) that exhibit high type-to-token ratios. In this work, we present a large-scale LM study on 50 typologically diverse languages covering a wide variety of morphological systems, and offer new LM benchmarks to the community, while considering subword-level information. The main technical contribution of our work is a novel method for injecting subword-level information into semantic word vectors, integrated into the neural language modeling training, to facilitate word-level prediction. We conduct experiments in the LM setting where the number of infrequent words is large, and demonstrate strong perplexity gains across our 50 languages, especially for morphologically-rich languages. Our code and data sets are publicly available.

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Deep Pivot-Based Modeling for Cross-language Cross-domain Transfer with Minimal Guidance
Yftah Ziser | Roi Reichart
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

While cross-domain and cross-language transfer have long been prominent topics in NLP research, their combination has hardly been explored. In this work we consider this problem, and propose a framework that builds on pivot-based learning, structure-aware Deep Neural Networks (particularly LSTMs and CNNs) and bilingual word embeddings, with the goal of training a model on labeled data from one (language, domain) pair so that it can be effectively applied to another (language, domain) pair. We consider two setups, differing with respect to the unlabeled data available for model training. In the full setup the model has access to unlabeled data from both pairs, while in the lazy setup, which is more realistic for truly resource-poor languages, unlabeled data is available for both domains but only for the source language. We design our model for the lazy setup so that for a given target domain, it can train once on the source language and then be applied to any target language without re-training. In experiments with nine English-German and nine English-French domain pairs our best model substantially outperforms previous models even when it is trained in the lazy setup and previous models are trained in the full setup.

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On the Relation between Linguistic Typology and (Limitations of) Multilingual Language Modeling
Daniela Gerz | Ivan Vulić | Edoardo Maria Ponti | Roi Reichart | Anna Korhonen
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

A key challenge in cross-lingual NLP is developing general language-independent architectures that are equally applicable to any language. However, this ambition is largely hampered by the variation in structural and semantic properties, i.e. the typological profiles of the world’s languages. In this work, we analyse the implications of this variation on the language modeling (LM) task. We present a large-scale study of state-of-the art n-gram based and neural language models on 50 typologically diverse languages covering a wide variety of morphological systems. Operating in the full vocabulary LM setup focused on word-level prediction, we demonstrate that a coarse typology of morphological systems is predictive of absolute LM performance. Moreover, fine-grained typological features such as exponence, flexivity, fusion, and inflectional synthesis are borne out to be responsible for the proliferation of low-frequency phenomena which are organically difficult to model by statistical architectures, or for the meaning ambiguity of character n-grams. Our study strongly suggests that these features have to be taken into consideration during the construction of next-level language-agnostic LM architectures, capable of handling morphologically complex languages such as Tamil or Korean.

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Neural Transition Based Parsing of Web Queries: An Entity Based Approach
Rivka Malca | Roi Reichart
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Web queries with question intent manifest a complex syntactic structure and the processing of this structure is important for their interpretation. Pinter et al. (2016) has formalized the grammar of these queries and proposed semi-supervised algorithms for the adaptation of parsers originally designed to parse according to the standard dependency grammar, so that they can account for the unique forest grammar of queries. However, their algorithms rely on resources typically not available outside of big web corporates. We propose a new BiLSTM query parser that: (1) Explicitly accounts for the unique grammar of web queries; and (2) Utilizes named entity (NE) information from a BiLSTM NE tagger, that can be jointly trained with the parser. In order to train our model we annotate the query treebank of Pinter et al. (2016) with NEs. When trained on 2500 annotated queries our parser achieves UAS of 83.5% and segmentation F1-score of 84.5, substantially outperforming existing state-of-the-art parsers.

2017

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Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP
Samuel Bowman | Yoav Goldberg | Felix Hill | Angeliki Lazaridou | Omer Levy | Roi Reichart | Anders Søgaard
Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP

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Automatic Selection of Context Configurations for Improved Class-Specific Word Representations
Ivan Vulić | Roy Schwartz | Ari Rappoport | Roi Reichart | Anna Korhonen
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

This paper is concerned with identifying contexts useful for training word representation models for different word classes such as adjectives (A), verbs (V), and nouns (N). We introduce a simple yet effective framework for an automatic selection of class-specific context configurations. We construct a context configuration space based on universal dependency relations between words, and efficiently search this space with an adapted beam search algorithm. In word similarity tasks for each word class, we show that our framework is both effective and efficient. Particularly, it improves the Spearman’s rho correlation with human scores on SimLex-999 over the best previously proposed class-specific contexts by 6 (A), 6 (V) and 5 (N) rho points. With our selected context configurations, we train on only 14% (A), 26.2% (V), and 33.6% (N) of all dependency-based contexts, resulting in a reduced training time. Our results generalise: we show that the configurations our algorithm learns for one English training setup outperform previously proposed context types in another training setup for English. Moreover, basing the configuration space on universal dependencies, it is possible to transfer the learned configurations to German and Italian. We also demonstrate improved per-class results over other context types in these two languages..

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Neural Structural Correspondence Learning for Domain Adaptation
Yftah Ziser | Roi Reichart
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

We introduce a neural network model that marries together ideas from two prominent strands of research on domain adaptation through representation learning: structural correspondence learning (SCL, (Blitzer et al., 2006)) and autoencoder neural networks (NNs). Our model is a three-layer NN that learns to encode the non-pivot features of an input example into a low dimensional representation, so that the existence of pivot features (features that are prominent in both domains and convey useful information for the NLP task) in the example can be decoded from that representation. The low-dimensional representation is then employed in a learning algorithm for the task. Moreover, we show how to inject pre-trained word embeddings into our model in order to improve generalization across examples with similar pivot features. We experiment with the task of cross-domain sentiment classification on 16 domain pairs and show substantial improvements over strong baselines.

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Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules
Ivan Vulić | Nikola Mrkšić | Roi Reichart | Diarmuid Ó Séaghdha | Steve Young | Anna Korhonen
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures. These effects are detrimental for language understanding systems, which may infer that ‘inexpensive’ is a rephrasing for ‘expensive’ or may not associate ‘acquire’ with ‘acquires’. In this work, we propose a novel morph-fitting procedure which moves past the use of curated semantic lexicons for improving distributional vector spaces. Instead, our method injects morphological constraints generated using simple language-specific rules, pulling inflectional forms of the same word close together and pushing derivational antonyms far apart. In intrinsic evaluation over four languages, we show that our approach: 1) improves low-frequency word estimates; and 2) boosts the semantic quality of the entire word vector collection. Finally, we show that morph-fitted vectors yield large gains in the downstream task of dialogue state tracking, highlighting the importance of morphology for tackling long-tail phenomena in language understanding tasks.

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Sarcasm SIGN: Interpreting Sarcasm with Sentiment Based Monolingual Machine Translation
Lotem Peled | Roi Reichart
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Sarcasm is a form of speech in which speakers say the opposite of what they truly mean in order to convey a strong sentiment. In other words, “Sarcasm is the giant chasm between what I say, and the person who doesn’t get it.”. In this paper we present the novel task of sarcasm interpretation, defined as the generation of a non-sarcastic utterance conveying the same message as the original sarcastic one. We introduce a novel dataset of 3000 sarcastic tweets, each interpreted by five human judges. Addressing the task as monolingual machine translation (MT), we experiment with MT algorithms and evaluation measures. We then present SIGN: an MT based sarcasm interpretation algorithm that targets sentiment words, a defining element of textual sarcasm. We show that while the scores of n-gram based automatic measures are similar for all interpretation models, SIGN’s interpretations are scored higher by humans for adequacy and sentiment polarity. We conclude with a discussion on future research directions for our new task.

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Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
Nikola Mrkšić | Ivan Vulić | Diarmuid Ó Séaghdha | Ira Leviant | Roi Reichart | Milica Gašić | Anna Korhonen | Steve Young
Transactions of the Association for Computational Linguistics, Volume 5

We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the use of constraints from mono- and cross-lingual resources, yielding semantically specialized cross-lingual vector spaces. Our evaluation shows that the method can make use of existing cross-lingual lexicons to construct high-quality vector spaces for a plethora of different languages, facilitating semantic transfer from high- to lower-resource ones. The effectiveness of our approach is demonstrated with state-of-the-art results on semantic similarity datasets in six languages. We next show that Attract-Repel-specialized vectors boost performance in the downstream task of dialogue state tracking (DST) across multiple languages. Finally, we show that cross-lingual vector spaces produced by our algorithm facilitate the training of multilingual DST models, which brings further performance improvements.

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Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets
Rotem Dror | Gili Baumer | Marina Bogomolov | Roi Reichart
Transactions of the Association for Computational Linguistics, Volume 5

With the ever growing amount of textual data from a large variety of languages, domains, and genres, it has become standard to evaluate NLP algorithms on multiple datasets in order to ensure a consistent performance across heterogeneous setups. However, such multiple comparisons pose significant challenges to traditional statistical analysis methods in NLP and can lead to erroneous conclusions. In this paper we propose a Replicability Analysis framework for a statistically sound analysis of multiple comparisons between algorithms for NLP tasks. We discuss the theoretical advantages of this framework over the current, statistically unjustified, practice in the NLP literature, and demonstrate its empirical value across four applications: multi-domain dependency parsing, multilingual POS tagging, cross-domain sentiment classification and word similarity prediction.

2016

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The Structured Weighted Violations Perceptron Algorithm
Rotem Dror | Roi Reichart
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Effective Greedy Inference for Graph-based Non-Projective Dependency Parsing
Ilan Tchernowitz | Liron Yedidsion | Roi Reichart
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity
Daniela Gerz | Ivan Vulić | Felix Hill | Roi Reichart | Anna Korhonen
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Edge-Linear First-Order Dependency Parsing with Undirected Minimum Spanning Tree Inference
Effi Levi | Roi Reichart | Ari Rappoport
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Symmetric Patterns and Coordinations: Fast and Enhanced Representations of Verbs and Adjectives
Roy Schwartz | Roi Reichart | Ari Rappoport
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Syntactic Parsing of Web Queries with Question Intent
Yuval Pinter | Roi Reichart | Idan Szpektor
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Survey on the Use of Typological Information in Natural Language Processing
Helen O’Horan | Yevgeni Berzak | Ivan Vulić | Roi Reichart | Anna Korhonen
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In recent years linguistic typologies, which classify the world’s languages according to their functional and structural properties, have been widely used to support multilingual NLP. While the growing importance of typologies in supporting multilingual tasks has been recognised, no systematic survey of existing typological resources and their use in NLP has been published. This paper provides such a survey as well as discussion which we hope will both inform and inspire future work in the area.

2015

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SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation
Felix Hill | Roi Reichart | Anna Korhonen
Computational Linguistics, Volume 41, Issue 4 - December 2015

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Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL
Yevgeni Berzak | Roi Reichart | Boris Katz
Proceedings of the Nineteenth Conference on Computational Natural Language Learning

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Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction
Roy Schwartz | Roi Reichart | Ari Rappoport
Proceedings of the Nineteenth Conference on Computational Natural Language Learning

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Unsupervised Declarative Knowledge Induction for Constraint-Based Learning of Information Structure in Scientific Documents
Yufan Guo | Roi Reichart | Anna Korhonen
Transactions of the Association for Computational Linguistics, Volume 3

Inferring the information structure of scientific documents is useful for many NLP applications. Existing approaches to this task require substantial human effort. We propose a framework for constraint learning that reduces human involvement considerably. Our model uses topic models to identify latent topics and their key linguistic features in input documents, induces constraints from this information and maps sentences to their dominant information structure categories through a constrained unsupervised model. When the induced constraints are combined with a fully unsupervised model, the resulting model challenges existing lightly supervised feature-based models as well as unsupervised models that use manually constructed declarative knowledge. Our results demonstrate that useful declarative knowledge can be learned from data with very limited human involvement.

2014

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Multi-Modal Models for Concrete and Abstract Concept Meaning
Felix Hill | Roi Reichart | Anna Korhonen
Transactions of the Association for Computational Linguistics, Volume 2

Multi-modal models that learn semantic representations from both linguistic and perceptual input outperform language-only models on a range of evaluations, and better reflect human concept acquisition. Most perceptual input to such models corresponds to concrete noun concepts and the superiority of the multi-modal approach has only been established when evaluating on such concepts. We therefore investigate which concepts can be effectively learned by multi-modal models. We show that concreteness determines both which linguistic features are most informative and the impact of perceptual input in such models. We then introduce ridge regression as a means of propagating perceptual information from concrete nouns to more abstract concepts that is more robust than previous approaches. Finally, we present weighted gram matrix combination, a means of combining representations from distinct modalities that outperforms alternatives when both modalities are sufficiently rich.

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Reconstructing Native Language Typology from Foreign Language Usage
Yevgeni Berzak | Roi Reichart | Boris Katz
Proceedings of the Eighteenth Conference on Computational Natural Language Learning

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Minimally Supervised Classification to Semantic Categories using Automatically Acquired Symmetric Patterns
Roy Schwartz | Roi Reichart | Ari Rappoport
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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An Unsupervised Model for Instance Level Subcategorization Acquisition
Simon Baker | Roi Reichart | Anna Korhonen
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Improved Information Structure Analysis of Scientific Documents Through Discourse and Lexical Constraints
Yufan Guo | Roi Reichart | Anna Korhonen
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Improved Lexical Acquisition through DPP-based Verb Clustering
Roi Reichart | Anna Korhonen
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Learning to Map into a Universal POS Tagset
Yuan Zhang | Roi Reichart | Regina Barzilay | Amir Globerson
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Improved Parsing and POS Tagging Using Inter-Sentence Consistency Constraints
Alexander Rush | Roi Reichart | Michael Collins | Amir Globerson
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP
Omri Abend | Chris Biemann | Anna Korhonen | Ari Rappoport | Roi Reichart | Anders Søgaard
Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP

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A Diverse Dirichlet Process Ensemble for Unsupervised Induction of Syntactic Categories
Roi Reichart | Gal Elidan | Ari Rappoport
Proceedings of COLING 2012

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Document and Corpus Level Inference For Unsupervised and Transductive Learning of Information Structure of Scientific Documents
Roi Reichart | Anna Korhonen
Proceedings of COLING 2012: Posters

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CRAB Reader: A Tool for Analysis and Visualization of Argumentative Zones in Scientific Literature
Yufan Guo | Ilona Silins | Roi Reichart | Anna Korhonen
Proceedings of COLING 2012: Demonstration Papers

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Multi-Event Extraction Guided by Global Constraints
Roi Reichart | Regina Barzilay
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2011

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Proceedings of the First workshop on Unsupervised Learning in NLP
Omri Abend | Anna Korhonen | Ari Rappoport | Roi Reichart
Proceedings of the First workshop on Unsupervised Learning in NLP

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Neutralizing Linguistically Problematic Annotations in Unsupervised Dependency Parsing Evaluation
Roy Schwartz | Omri Abend | Roi Reichart | Ari Rappoport
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Confidence Driven Unsupervised Semantic Parsing
Dan Goldwasser | Roi Reichart | James Clarke | Dan Roth
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Improved Unsupervised POS Induction Using Intrinsic Clustering Quality and a Zipfian Constraint
Roi Reichart | Raanan Fattal | Ari Rappoport
Proceedings of the Fourteenth Conference on Computational Natural Language Learning

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Type Level Clustering Evaluation: New Measures and a POS Induction Case Study
Roi Reichart | Omri Abend | Ari Rappoport
Proceedings of the Fourteenth Conference on Computational Natural Language Learning

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Improved Unsupervised POS Induction through Prototype Discovery
Omri Abend | Roi Reichart | Ari Rappoport
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Tense Sense Disambiguation: A New Syntactic Polysemy Task
Roi Reichart | Ari Rappoport
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Improved Fully Unsupervised Parsing with Zoomed Learning
Roi Reichart | Ari Rappoport
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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A Multi-Domain Web-Based Algorithm for POS Tagging of Unknown Words
Shulamit Umansky-Pesin | Roi Reichart | Ari Rappoport
Coling 2010: Posters

2009

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Unsupervised Argument Identification for Semantic Role Labeling
Omri Abend | Roi Reichart | Ari Rappoport
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

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Sample Selection for Statistical Parsers: Cognitively Driven Algorithms and Evaluation Measures
Roi Reichart | Ari Rappoport
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

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Superior and Efficient Fully Unsupervised Pattern-based Concept Acquisition Using an Unsupervised Parser
Dmitry Davidov | Roi Reichart | Ari Rappoport
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

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Automatic Selection of High Quality Parses Created By a Fully Unsupervised Parser
Roi Reichart | Ari Rappoport
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

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The NVI Clustering Evaluation Measure
Roi Reichart | Ari Rappoport
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

2008

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A Supervised Algorithm for Verb Disambiguation into VerbNet Classes
Omri Abend | Roi Reichart | Ari Rappoport
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Unsupervised Induction of Labeled Parse Trees by Clustering with Syntactic Features
Roi Reichart | Ari Rappoport
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Multi-Task Active Learning for Linguistic Annotations
Roi Reichart | Katrin Tomanek | Udo Hahn | Ari Rappoport
Proceedings of ACL-08: HLT

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Extraction of Entailed Semantic Relations Through Syntax-Based Comma Resolution
Vivek Srikumar | Roi Reichart | Mark Sammons | Ari Rappoport | Dan Roth
Proceedings of ACL-08: HLT

2007

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An Ensemble Method for Selection of High Quality Parses
Roi Reichart | Ari Rappoport
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

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Self-Training for Enhancement and Domain Adaptation of Statistical Parsers Trained on Small Datasets
Roi Reichart | Ari Rappoport
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

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