Reut Tsarfaty


2021

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Asking It All: Generating Contextualized Questions for any Semantic Role
Valentina Pyatkin | Paul Roit | Julian Michael | Yoav Goldberg | Reut Tsarfaty | Ido Dagan
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Asking questions about a situation is an inherent step towards understanding it. To this end, we introduce the task of role question generation, which, given a predicate mention and a passage, requires producing a set of questions asking about all possible semantic roles of the predicate. We develop a two-stage model for this task, which first produces a context-independent question prototype for each role and then revises it to be contextually appropriate for the passage. Unlike most existing approaches to question generation, our approach does not require conditioning on existing answers in the text. Instead, we condition on the type of information to inquire about, regardless of whether the answer appears explicitly in the text, could be inferred from it, or should be sought elsewhere. Our evaluation demonstrates that we generate diverse and well-formed questions for a large, broad-coverage ontology of predicates and roles.

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Minimal Supervision for Morphological Inflection
Omer Goldman | Reut Tsarfaty
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Neural models for the various flavours of morphological reinflection tasks have proven to be extremely accurate given ample labeled data, yet labeled data may be slow and costly to obtain. In this work we aim to overcome this annotation bottleneck by bootstrapping labeled data from a seed as small as five labeled inflection tables, accompanied by a large bulk of unlabeled text. Our bootstrapping method exploits the orthographic and semantic regularities in morphological systems in a two-phased setup, where word tagging based on analogies is followed by word pairing based on distances. Our experiments with the Paradigm Cell Filling Problem over eight typologically different languages show that in languages with relatively simple morphology, orthographic regularities on their own allow inflection models to achieve respectable accuracy. Combined orthographic and semantic regularities alleviate difficulties with particularly complex morpho-phonological systems. We further show that our bootstrapping methods substantially outperform hallucination-based methods commonly used for overcoming the annotation bottleneck in morphological reinflection tasks.

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Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)
Royi Lachmy | Ziyu Yao | Greg Durrett | Milos Gligoric | Junyi Jessy Li | Ray Mooney | Graham Neubig | Yu Su | Huan Sun | Reut Tsarfaty
Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)

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Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Paola Merlo | Jorg Tiedemann | Reut Tsarfaty
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)
Stephan Oepen | Kenji Sagae | Reut Tsarfaty | Gosse Bouma | Djam\'e Seddah | Daniel Zeman
Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)

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Well-Defined Morphology is Sentence-Level Morphology
Omer Goldman | Reut Tsarfaty
Proceedings of the 1st Workshop on Multilingual Representation Learning

Morphological tasks have gained decent popularity within the NLP community in the recent years, with large multi-lingual datasets providing morphological analysis of words, either in or out of context. However, the lack of a clear linguistic definition for words destines the annotative work to be incomplete and mired in inconsistencies, especially cross-linguistically. In this work we expand morphological inflection of words to inflection of sentences to provide true universality disconnected from orthographic traditions of white-space usage. To allow annotation for sentence-inflection we define a morphological annotation scheme by a fixed set of inflectional features. We present a small cross-linguistic dataset including semi-manually generated simple sentences in 4 typologically diverse languages annotated according to our suggested scheme, and show that the task of reinflection gets substantially more difficult but that the change of scope from words to well-defined sentences allows interface with contextualized language models.

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Proceedings of the 1st Workshop on Understanding Implicit and Underspecified Language
Michael Roth | Reut Tsarfaty | Yoav Goldberg
Proceedings of the 1st Workshop on Understanding Implicit and Underspecified Language

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The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing
Valentina Pyatkin | Shoval Sadde | Aynat Rubinstein | Paul Portner | Reut Tsarfaty
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)

Modality is the linguistic ability to describe vents with added information such as how desirable, plausible, or feasible they are. Modality is important for many NLP downstream tasks such as the detection of hedging, uncertainty, speculation, and more. Previous studies that address modality detection in NLP often restrict modal expressions to a closed syntactic class, and the modal sense labels are vastly different across different studies, lacking an accepted standard. Furthermore, these senses are often analyzed independently of the events that they modify. This work builds on the theoretical foundations of the Georgetown Gradable Modal Expressions (GME) work by Rubinstein et al. (2013) to propose an event-based modality detection task where modal expressions can be words of any syntactic class and sense labels are drawn from a comprehensive taxonomy which harmonizes the modal concepts contributed by the different studies. We present experiments on the GME corpus aiming to detect and classify fine-grained modal concepts and associate them with their modified events. We show that detecting and classifying modal expressions is not only feasible, it also improves the detection of modal events in their own right.

2020

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From SPMRL to NMRL: What Did We Learn (and Unlearn) in a Decade of Parsing Morphologically-Rich Languages (MRLs)?
Reut Tsarfaty | Dan Bareket | Stav Klein | Amit Seker
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

It has been exactly a decade since the first establishment of SPMRL, a research initiative unifying multiple research efforts to address the peculiar challenges of Statistical Parsing for Morphologically-Rich Languages (MRLs). Here we reflect on parsing MRLs in that decade, highlight the solutions and lessons learned for the architectural, modeling and lexical challenges in the pre-neural era, and argue that similar challenges re-emerge in neural architectures for MRLs. We then aim to offer a climax, suggesting that incorporating symbolic ideas proposed in SPMRL terms into nowadays neural architectures has the potential to push NLP for MRLs to a new level. We sketch a strategies for designing Neural Models for MRLs (NMRL), and showcase preliminary support for these strategies via investigating the task of multi-tagging in Hebrew, a morphologically-rich, high-fusion, language.

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pyBART: Evidence-based Syntactic Transformations for IE
Aryeh Tiktinsky | Yoav Goldberg | Reut Tsarfaty
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Syntactic dependencies can be predicted with high accuracy, and are useful for both machine-learned and pattern-based information extraction tasks. However, their utility can be improved. These syntactic dependencies are designed to accurately reflect syntactic relations, and they do not make semantic relations explicit. Therefore, these representations lack many explicit connections between content words, that would be useful for downstream applications. Proposals like English Enhanced UD improve the situation by extending universal dependency trees with additional explicit arcs. However, they are not available to Python users, and are also limited in coverage. We introduce a broad-coverage, data-driven and linguistically sound set of transformations, that makes event-structure and many lexical relations explicit. We present pyBART, an easy-to-use open-source Python library for converting English UD trees either to Enhanced UD graphs or to our representation. The library can work as a standalone package or be integrated within a spaCy NLP pipeline. When evaluated in a pattern-based relation extraction scenario, our representation results in higher extraction scores than Enhanced UD, while requiring fewer patterns.

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Getting the ##life out of living: How Adequate Are Word-Pieces for Modelling Complex Morphology?
Stav Klein | Reut Tsarfaty
Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

This work investigates the most basic units that underlie contextualized word embeddings, such as BERT — the so-called word pieces. In Morphologically-Rich Languages (MRLs) which exhibit morphological fusion and non-concatenative morphology, the different units of meaning within a word may be fused, intertwined, and cannot be separated linearly. Therefore, when using word-pieces in MRLs, we must consider that: (1) a linear segmentation into sub-word units might not capture the full morphological complexity of words; and (2) representations that leave morphological knowledge on sub-word units inaccessible might negatively affect performance. Here we empirically examine the capacity of word-pieces to capture morphology by investigating the task of multi-tagging in Modern Hebrew, as a proxy to evaluate the underlying segmentation. Our results show that, while models trained to predict multi-tags for complete words outperform models tuned to predict the distinct tags of WPs, we can improve the WPs tag prediction by purposefully constraining the word-pieces to reflect their internal functions. We suggest that linguistically-informed word-pieces schemes, that make the morphological structure explicit, might boost performance for MRLs.

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Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies
Gosse Bouma | Yuji Matsumoto | Stephan Oepen | Kenji Sagae | Djamé Seddah | Weiwei Sun | Anders Søgaard | Reut Tsarfaty | Dan Zeman
Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies

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QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines
Valentina Pyatkin | Ayal Klein | Reut Tsarfaty | Ido Dagan
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Discourse relations describe how two propositions relate to one another, and identifying them automatically is an integral part of natural language understanding. However, annotating discourse relations typically requires expert annotators. Recently, different semantic aspects of a sentence have been represented and crowd-sourced via question-and-answer (QA) pairs. This paper proposes a novel representation of discourse relations as QA pairs, which in turn allows us to crowd-source wide-coverage data annotated with discourse relations, via an intuitively appealing interface for composing such questions and answers. Based on our proposed representation, we collect a novel and wide-coverage QADiscourse dataset, and present baseline algorithms for predicting QADiscourse relations.

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ZEST: Zero-shot Learning from Text Descriptions using Textual Similarity and Visual Summarization
Tzuf Paz-Argaman | Reut Tsarfaty | Gal Chechik | Yuval Atzmon
Findings of the Association for Computational Linguistics: EMNLP 2020

We study the problem of recognizing visual entities from the textual descriptions of their classes. Specifically, given birds’ images with free-text descriptions of their species, we learn to classify images of previously-unseen species based on specie descriptions. This setup has been studied in the vision community under the name zero-shot learning from text, focusing on learning to transfer knowledge about visual aspects of birds from seen classes to previously-unseen ones. Here, we suggest focusing on the textual description and distilling from the description the most relevant information to effectively match visual features to the parts of the text that discuss them. Specifically, (1) we propose to leverage the similarity between species, reflected in the similarity between text descriptions of the species. (2) we derive visual summaries of the texts, i.e., extractive summaries that focus on the visual features that tend to be reflected in images. We propose a simple attention-based model augmented with the similarity and visual summaries components. Our empirical results consistently and significantly outperform the state-of-the-art on the largest benchmarks for text-based zero-shot learning, illustrating the critical importance of texts for zero-shot image-recognition.

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Evaluating Models’ Local Decision Boundaries via Contrast Sets
Matt Gardner | Yoav Artzi | Victoria Basmov | Jonathan Berant | Ben Bogin | Sihao Chen | Pradeep Dasigi | Dheeru Dua | Yanai Elazar | Ananth Gottumukkala | Nitish Gupta | Hannaneh Hajishirzi | Gabriel Ilharco | Daniel Khashabi | Kevin Lin | Jiangming Liu | Nelson F. Liu | Phoebe Mulcaire | Qiang Ning | Sameer Singh | Noah A. Smith | Sanjay Subramanian | Reut Tsarfaty | Eric Wallace | Ally Zhang | Ben Zhou
Findings of the Association for Computational Linguistics: EMNLP 2020

Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture the abilities a dataset is intended to test. We propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data. In particular, after a dataset is constructed, we recommend that the dataset authors manually perturb the test instances in small but meaningful ways that (typically) change the gold label, creating contrast sets. Contrast sets provide a local view of a model’s decision boundary, which can be used to more accurately evaluate a model’s true linguistic capabilities. We demonstrate the efficacy of contrast sets by creating them for 10 diverse NLP datasets (e.g., DROP reading comprehension, UD parsing, and IMDb sentiment analysis). Although our contrast sets are not explicitly adversarial, model performance is significantly lower on them than on the original test sets—up to 25% in some cases. We release our contrast sets as new evaluation benchmarks and encourage future dataset construction efforts to follow similar annotation processes.

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A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration
Avi Shmidman | Joshua Guedalia | Shaltiel Shmidman | Moshe Koppel | Reut Tsarfaty
Findings of the Association for Computational Linguistics: EMNLP 2020

One of the primary tasks of morphological parsers is the disambiguation of homographs. Particularly difficult are cases of unbalanced ambiguity, where one of the possible analyses is far more frequent than the others. In such cases, there may not exist sufficient examples of the minority analyses in order to properly evaluate performance, nor to train effective classifiers. In this paper we address the issue of unbalanced morphological ambiguities in Hebrew. We offer a challenge set for Hebrew homographs — the first of its kind — containing substantial attestation of each analysis of 21 Hebrew homographs. We show that the current SOTA of Hebrew disambiguation performs poorly on cases of unbalanced ambiguity. Leveraging our new dataset, we achieve a new state-of-the-art for all 21 words, improving the overall average F1 score from 0.67 to 0.95. Our resulting annotated datasets are made publicly available for further research.

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A Pointer Network Architecture for Joint Morphological Segmentation and Tagging
Amit Seker | Reut Tsarfaty
Findings of the Association for Computational Linguistics: EMNLP 2020

Morphologically Rich Languages (MRLs) such as Arabic, Hebrew and Turkish often require Morphological Disambiguation (MD), i.e., the prediction of morphological decomposition of tokens into morphemes, early in the pipeline. Neural MD may be addressed as a simple pipeline, where segmentation is followed by sequence tagging, or as an end-to-end model, predicting morphemes from raw tokens. Both approaches are sub-optimal; the former is heavily prone to error propagation, and the latter does not enjoy explicit access to the basic processing units called morphemes. This paper offers MD architecture that combines the symbolic knowledge of morphemes with the learning capacity of neural end-to-end modeling. We propose a new, general and easy-to-implement Pointer Network model where the input is a morphological lattice and the output is a sequence of indices pointing at a single disambiguated path of morphemes. We demonstrate the efficacy of the model on segmentation and tagging, for Hebrew and Turkish texts, based on their respective Universal Dependencies (UD) treebanks. Our experiments show that with complete lattices, our model outperforms all shared-task results on segmenting and tagging these languages. On the SPMRL treebank, our model outperforms all previously reported results for Hebrew MD in realistic scenarios.

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The Extraordinary Failure of Complement Coercion Crowdsourcing
Yanai Elazar | Victoria Basmov | Shauli Ravfogel | Yoav Goldberg | Reut Tsarfaty
Proceedings of the First Workshop on Insights from Negative Results in NLP

Crowdsourcing has eased and scaled up the collection of linguistic annotation in recent years. In this work, we follow known methodologies of collecting labeled data for the complement coercion phenomenon. These are constructions with an implied action — e.g., “I started a new book I bought last week”, where the implied action is reading. We aim to collect annotated data for this phenomenon by reducing it to either of two known tasks: Explicit Completion and Natural Language Inference. However, in both cases, crowdsourcing resulted in low agreement scores, even though we followed the same methodologies as in previous work. Why does the same process fail to yield high agreement scores? We specify our modeling schemes, highlight the differences with previous work and provide some insights about the task and possible explanations for the failure. We conclude that specific phenomena require tailored solutions, not only in specialized algorithms, but also in data collection methods.

2019

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Joint Transition-Based Models for Morpho-Syntactic Parsing: Parsing Strategies for MRLs and a Case Study from Modern Hebrew
Amir More | Amit Seker | Victoria Basmova | Reut Tsarfaty
Transactions of the Association for Computational Linguistics, Volume 7

In standard NLP pipelines, morphological analysis and disambiguation (MA&D) precedes syntactic and semantic downstream tasks. However, for languages with complex and ambiguous word-internal structure, known as morphologically rich languages (MRLs), it has been hypothesized that syntactic context may be crucial for accurate MA&D, and vice versa. In this work we empirically confirm this hypothesis for Modern Hebrew, an MRL with complex morphology and severe word-level ambiguity, in a novel transition-based framework. Specifically, we propose a joint morphosyntactic transition-based framework which formally unifies two distinct transition systems, morphological and syntactic, into a single transition-based system with joint training and joint inference. We empirically show that MA&D results obtained in the joint settings outperform MA&D results obtained by the respective standalone components, and that end-to-end parsing results obtained by our joint system present a new state of the art for Hebrew dependency parsing.

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RUN through the Streets: A New Dataset and Baseline Models for Realistic Urban Navigation
Tzuf Paz-Argaman | Reut Tsarfaty
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Following navigation instructions in natural language (NL) requires a composition of language, action, and knowledge of the environment. Knowledge of the environment may be provided via visual sensors or as a symbolic world representation referred to as a map. Previous work on map-based NL navigation relied on small artificial worlds with a fixed set of entities known in advance. Here we introduce the Realistic Urban Navigation (RUN) task, aimed at interpreting NL navigation instructions based on a real, dense, urban map. Using Amazon Mechanical Turk, we collected a dataset of 2515 instructions aligned with actual routes over three regions of Manhattan. We then empirically study which aspects of a neural architecture are important for the RUN success, and empirically show that entity abstraction, attention over words and worlds, and a constantly updating world-state, significantly contribute to task accuracy.

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What’s Wrong with Hebrew NLP? And How to Make it Right
Reut Tsarfaty | Shoval Sadde | Stav Klein | Amit Seker
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

For languages with simple morphology such as English, automatic annotation pipelines such as spaCy or Stanford’s CoreNLP successfully serve projects in academia and the industry. For many morphologically-rich languages (MRLs), similar pipelines show sub-optimal performance that limits their applicability for text analysis in research and the industry. The sub-optimal performance is mainly due to errors in early morphological disambiguation decisions, that cannot be recovered later on in the pipeline, yielding incoherent annotations on the whole. This paper describes the design and use of the ONLP suite, a joint morpho-syntactic infrastructure for processing Modern Hebrew texts. The joint inference over morphology and syntax substantially limits error propagation, and leads to high accuracy. ONLP provides rich and expressive annotations which already serve diverse academic and commercial needs. Its accompanying demo further serves educational activities, introducing Hebrew NLP intricacies to researchers and non-researchers alike.

2018

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Universal Morpho-Syntactic Parsing and the Contribution of Lexica: Analyzing the ONLP Lab Submission to the CoNLL 2018 Shared Task
Amit Seker | Amir More | Reut Tsarfaty
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

We present the contribution of the ONLP lab at the Open University of Israel to the UD shared task on multilingual parsing from raw text to Universal Dependencies. Our contribution is based on a transition-based parser called ‘yap – yet another parser’, which includes a standalone morphological model, a standalone dependency model, and a joint morphosyntactic model. In the task we used yap‘s standalone dependency parser to parse input morphologically disambiguated by UDPipe, and obtained the official score of 58.35 LAS. In our follow up investigation we use yap to show how the incorporation of morphological and lexical resources may improve the performance of end-to-end raw-to-dependencies parsing in the case of a morphologically-rich and low-resource language, Modern Hebrew. Our results on Hebrew underscore the importance of CoNLL-UL, a UD-compatible standard for accessing external lexical resources, for enhancing end-to-end UD parsing, in particular for morphologically rich and low-resource languages. We thus encourage the community to create, convert, or make available more such lexica in future tasks.

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CoNLL-UL: Universal Morphological Lattices for Universal Dependency Parsing
Amir More | Özlem Çetinoğlu | Çağrı Çöltekin | Nizar Habash | Benoît Sagot | Djamé Seddah | Dima Taji | Reut Tsarfaty
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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The Hebrew Universal Dependency Treebank: Past Present and Future
Shoval Sade | Amit Seker | Reut Tsarfaty
Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)

The Hebrew treebank (HTB), consisting of 6221 morpho-syntactically annotated newspaper sentences, has been the only resource for training and validating statistical parsers and taggers for Hebrew, for almost two decades now. During these decades, the HTB has gone through a trajectory of automatic and semi-automatic conversions, until arriving at its UDv2 form. In this work we manually validate the UDv2 version of the HTB, and, according to our findings, we apply scheme changes that bring the UD HTB to the same theoretical grounds as the rest of UD. Our experimental parsing results with UDv2New confirm that improving the coherence and internal consistency of the UD HTB indeed leads to improved parsing performance. At the same time, our analysis demonstrates that there is more to be done at the point of intersection of UD with other linguistic processing layers, in particular, at the points where UD interfaces external morphological and lexical resources.

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Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from Modern Hebrew
Adam Amram | Anat Ben David | Reut Tsarfaty
Proceedings of the 27th International Conference on Computational Linguistics

This paper empirically studies the effects of representation choices on neural sentiment analysis for Modern Hebrew, a morphologically rich language (MRL) for which no sentiment analyzer currently exists. We study two dimensions of representational choices: (i) the granularity of the input signal (token-based vs. morpheme-based), and (ii) the level of encoding of vocabulary items (string-based vs. character-based). We hypothesise that for MRLs, languages where multiple meaning-bearing elements may be carried by a single space-delimited token, these choices will have measurable effects on task perfromance, and that these effects may vary for different architectural designs — fully-connected, convolutional or recurrent. Specifically, we hypothesize that morpheme-based representations will have advantages in terms of their generalization capacity and task accuracy, due to their better OOV coverage. To empirically study these effects, we develop a new sentiment analysis benchmark for Hebrew, based on 12K social media comments, and provide two instances of these data: in token-based and morpheme-based settings. Our experiments show that representation choices empirical effects vary with architecture type. While fully-connected and convolutional networks slightly prefer token-based settings, RNNs benefit from a morpheme-based representation, in accord with the hypothesis that explicit morphological information may help generalize. Our endeavour also delivers the first state-of-the-art broad-coverage sentiment analyzer for Hebrew, with over 89% accuracy, alongside an established benchmark to further study the effects of linguistic representation choices on neural networks’ task performance.

2017

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Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation (ONLG)
Tomer Cagan | Stefan L. Frank | Reut Tsarfaty
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Opinionated Natural Language Generation (ONLG) is a new, challenging, task that aims to automatically generate human-like, subjective, responses to opinionated articles online. We present a data-driven architecture for ONLG that generates subjective responses triggered by users’ agendas, consisting of topics and sentiments, and based on wide-coverage automatically-acquired generative grammars. We compare three types of grammatical representations that we design for ONLG, which interleave different layers of linguistic information and are induced from a new, enriched dataset we developed. Our evaluation shows that generation with Relational-Realizational (Tsarfaty and Sima’an, 2008) inspired grammar gets better language model scores than lexicalized grammars ‘a la Collins (2003), and that the latter gets better human-evaluation scores. We also show that conditioning the generation on topic models makes generated responses more relevant to the document content.

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Universal Joint Morph-Syntactic Processing: The Open University of Israel’s Submission to The CoNLL 2017 Shared Task
Amir More | Reut Tsarfaty
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

We present the Open University’s submission to the CoNLL 2017 Shared Task on multilingual parsing from raw text to Universal Dependencies. The core of our system is a joint morphological disambiguator and syntactic parser which accepts morphologically analyzed surface tokens as input and returns morphologically disambiguated dependency trees as output. Our parser requires a lattice as input, so we generate morphological analyses of surface tokens using a data-driven morphological analyzer that derives its lexicon from the UD training corpora, and we rely on UDPipe for sentence segmentation and surface-level tokenization. We report our official macro-average LAS is 56.56. Although our model is not as performant as many others, it does not make use of neural networks, therefore we do not rely on word embeddings or any other data source other than the corpora themselves. In addition, we show the utility of a lexicon-backed morphological analyzer for the MRL Modern Hebrew. We use our results on Modern Hebrew to argue that the UD community should define a UD-compatible standard for access to lexical resources, which we argue is crucial for MRLs and low resource languages in particular.

2016

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Data-Driven Morphological Analysis and Disambiguation for Morphologically Rich Languages and Universal Dependencies
Amir More | Reut Tsarfaty
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Parsing texts into universal dependencies (UD) in realistic scenarios requires infrastructure for the morphological analysis and disambiguation (MA&D) of typologically different languages as a first tier. MA&D is particularly challenging in morphologically rich languages (MRLs), where the ambiguous space-delimited tokens ought to be disambiguated with respect to their constituent morphemes, each morpheme carrying its own tag and a rich set features. Here we present a novel, language-agnostic, framework for MA&D, based on a transition system with two variants — word-based and morpheme-based — and a dedicated transition to mitigate the biases of variable-length morpheme sequences. Our experiments on a Modern Hebrew case study show state of the art results, and we show that the morpheme-based MD consistently outperforms our word-based variant. We further illustrate the utility and multilingual coverage of our framework by morphologically analyzing and disambiguating the large set of languages in the UD treebanks.

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Universal Dependencies v1: A Multilingual Treebank Collection
Joakim Nivre | Marie-Catherine de Marneffe | Filip Ginter | Yoav Goldberg | Jan Hajič | Christopher D. Manning | Ryan McDonald | Slav Petrov | Sampo Pyysalo | Natalia Silveira | Reut Tsarfaty | Daniel Zeman
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Cross-linguistically consistent annotation is necessary for sound comparative evaluation and cross-lingual learning experiments. It is also useful for multilingual system development and comparative linguistic studies. Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. In this paper, we describe v1 of the universal guidelines, the underlying design principles, and the currently available treebanks for 33 languages.

2014

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Generating Subjective Responses to Opinionated Articles in Social Media: An Agenda-Driven Architecture and a Turing-Like Test
Tomer Cagan | Stefan L. Frank | Reut Tsarfaty
Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media

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Introducing the SPMRL 2014 Shared Task on Parsing Morphologically-rich Languages
Djamé Seddah | Sandra Kübler | Reut Tsarfaty
Proceedings of the First Joint Workshop on Statistical Parsing of Morphologically Rich Languages and Syntactic Analysis of Non-Canonical Languages

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Semantic Parsing Using Content and Context: A Case Study from Requirements Elicitation
Reut Tsarfaty | Ilia Pogrebezky | Guy Weiss | Yaarit Natan | Smadar Szekely | David Harel
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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A Unified Morpho-Syntactic Scheme of Stanford Dependencies
Reut Tsarfaty
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Parsing Morphologically Rich Languages: Introduction to the Special Issue
Reut Tsarfaty | Djamé Seddah | Sandra Kübler | Joakim Nivre
Computational Linguistics, Volume 39, Issue 1 - March 2013

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Book Review: Design Patterns in Fluid Construction Grammar edited by Luc Steels
Nathan Schneider | Reut Tsarfaty
Computational Linguistics, Volume 39, Issue 2 - June 2013

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Overview of the SPMRL 2013 Shared Task: A Cross-Framework Evaluation of Parsing Morphologically Rich Languages
Djamé Seddah | Reut Tsarfaty | Sandra Kübler | Marie Candito | Jinho D. Choi | Richárd Farkas | Jennifer Foster | Iakes Goenaga | Koldo Gojenola Galletebeitia | Yoav Goldberg | Spence Green | Nizar Habash | Marco Kuhlmann | Wolfgang Maier | Joakim Nivre | Adam Przepiórkowski | Ryan Roth | Wolfgang Seeker | Yannick Versley | Veronika Vincze | Marcin Woliński | Alina Wróblewska | Eric Villemonte de la Clergerie
Proceedings of the Fourth Workshop on Statistical Parsing of Morphologically-Rich Languages

2012

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Joint Evaluation of Morphological Segmentation and Syntactic Parsing
Reut Tsarfaty | Joakim Nivre | Evelina Andersson
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Cross-Framework Evaluation for Statistical Parsing
Reut Tsarfaty | Joakim Nivre | Evelina Andersson
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

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Proceedings of the ACL 2012 Joint Workshop on Statistical Parsing and Semantic Processing of Morphologically Rich Languages
Marianna Apidianaki | Ido Dagan | Jennifer Foster | Yuval Marton | Djamé Seddah | Reut Tsarfaty
Proceedings of the ACL 2012 Joint Workshop on Statistical Parsing and Semantic Processing of Morphologically Rich Languages

2011

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Evaluating Dependency Parsing: Robust and Heuristics-Free Cross-Annotation Evaluation
Reut Tsarfaty | Joakim Nivre | Evelina Andersson
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the Second Workshop on Statistical Parsing of Morphologically Rich Languages
Djamé Seddah | Reut Tsarfaty | Jennifer Foster
Proceedings of the Second Workshop on Statistical Parsing of Morphologically Rich Languages

2010

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Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
Djame Seddah | Sandra Koebler | Reut Tsarfaty
Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages

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Statistical Parsing of Morphologically Rich Languages (SPMRL) What, How and Whither
Reut Tsarfaty | Djamé Seddah | Yoav Goldberg | Sandra Kuebler | Yannick Versley | Marie Candito | Jennifer Foster | Ines Rehbein | Lamia Tounsi
Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages

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Modeling Morphosyntactic Agreement in Constituency-Based Parsing of Modern Hebrew
Reut Tsarfaty | Khalil Sima’an
Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages

2009

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An Alternative to Head-Driven Approaches for Parsing a (Relatively) Free Word-Order Language
Reut Tsarfaty | Khalil Sima’an | Remko Scha
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Enhancing Unlexicalized Parsing Performance Using a Wide Coverage Lexicon, Fuzzy Tag-Set Mapping, and EM-HMM-Based Lexical Probabilities
Yoav Goldberg | Reut Tsarfaty | Meni Adler | Michael Elhadad
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

2008

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A Single Generative Model for Joint Morphological Segmentation and Syntactic Parsing
Yoav Goldberg | Reut Tsarfaty
Proceedings of ACL-08: HLT

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Word-Based or Morpheme-Based? Annotation Strategies for Modern Hebrew Clitics
Reut Tsarfaty | Yoav Goldberg
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Morphologically rich languages pose a challenge to the annotators of treebanks with respect to the status of orthographic (space-delimited) words in the syntactic parse trees. In such languages an orthographic word may carry various, distinct, sorts of information and the question arises whether we should represent such words as a sequence of their constituent morphemes (i.e., a Morpheme-Based annotation strategy) or whether we should preserve their special orthographic status within the trees (i.e., a Word-Based annotation strategy). In this paper we empirically address this challenge in the context of the development of Language Resources for Modern Hebrew. We compare and contrast the Morpheme-Based and Word-Based annotation strategies of pronominal clitics in Modern Hebrew and we show that the Word-Based strategy is more adequate for the purpose of training statistical parsers as it provides a better PP-attachment disambiguation capacity and a better alignment with initial surface forms. Our findings in turn raise new questions concerning the interaction of morphological and syntactic processing of which investigation is facilitated by the parallel treebank we made available.

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Relational-Realizational Parsing
Reut Tsarfaty | Khalil Sima’an
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

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Three-Dimensional Parametrization for Parsing Morphologically Rich Languages
Reut Tsarfaty | Khalil Sima’an
Proceedings of the Tenth International Conference on Parsing Technologies

2006

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Integrated Morphological and Syntactic Disambiguation for Modern Hebrew
Reut Tsarfaty
Proceedings of the COLING/ACL 2006 Student Research Workshop

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