Afsaneh Fazly


2021

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Dependency parsing with structure preserving embeddings
Ákos Kádár | Lan Xiao | Mete Kemertas | Federico Fancellu | Allan Jepson | Afsaneh Fazly
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Modern neural approaches to dependency parsing are trained to predict a tree structure by jointly learning a contextual representation for tokens in a sentence, as well as a head–dependent scoring function. Whereas this strategy results in high performance, it is difficult to interpret these representations in relation to the geometry of the underlying tree structure. Our work seeks instead to learn interpretable representations by training a parser to explicitly preserve structural properties of a tree. We do so by casting dependency parsing as a tree embedding problem where we incorporate geometric properties of dependency trees in the form of training losses within a graph-based parser. We provide a thorough evaluation of these geometric losses, showing that a majority of them yield strong tree distance preservation as well as parsing performance on par with a competitive graph-based parser (Qi et al., 2018). Finally, we show where parsing errors lie in terms of tree relationship in order to guide future work.

2020

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How coherent are neural models of coherence?
Leila Pishdad | Federico Fancellu | Ran Zhang | Afsaneh Fazly
Proceedings of the 28th International Conference on Computational Linguistics

Despite the recent advances in coherence modelling, most such models including state-of-the-art neural ones, are evaluated on either contrived proxy tasks such as the standard order discrimination benchmark, or tasks that require special expert annotation. Moreover, most evaluations are conducted on small newswire corpora. To address these shortcomings, in this paper we propose four generic evaluation tasks that draw on different aspects of coherence at both the lexical and document levels, and can be applied to any corpora. In designing these tasks, we aim at capturing coherence-specific properties, such as the correct use of discourse connectives, lexical cohesion, as well as the overall temporal and causal consistency among events and participants in a story. Importantly, our proposed tasks either rely on automatically-generated data, or data annotated for other purposes, hence alleviating the need for annotation specifically targeted to the task of coherence modelling. We perform experiments with several existing state-of-the-art neural models of coherence on these tasks, across large corpora from different domains, including newswire, dialogue, as well as narrative and instructional text. Our findings point to a strong need for revisiting the common practices in the development and evaluation of coherence models.

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Accurate polyglot semantic parsing with DAG grammars
Federico Fancellu | Ákos Kádár | Ran Zhang | Afsaneh Fazly
Findings of the Association for Computational Linguistics: EMNLP 2020

Semantic parses are directed acyclic graphs (DAGs), but in practice most parsers treat them as strings or trees, mainly because models that predict graphs are far less understood. This simplification, however, comes at a cost: there is no guarantee that the output is a well-formed graph. A recent work by Fancellu et al. (2019) addressed this problem by proposing a graph-aware sequence model that utilizes a DAG grammar to guide graph generation. We significantly improve upon this work, by proposing a simpler architecture as well as more efficient training and inference algorithms that can always guarantee the well-formedness of the generated graphs. Importantly, unlike Fancellu et al., our model does not require language-specific features, and hence can harness the inherent ability of DAG-grammar parsing in multilingual settings. We perform monolingual as well as multilingual experiments on the Parallel Meaning Bank (Abzianidze et al., 2017). Our parser outperforms previous graph-aware models by a large margin, and closes the performance gap between string-based and DAG-grammar parsing.

2017

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Investigating the Opacity of Verb-Noun Multiword Expression Usages in Context
Shiva Taslimipoor | Omid Rohanian | Ruslan Mitkov | Afsaneh Fazly
Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)

This study investigates the supervised token-based identification of Multiword Expressions (MWEs). This is an ongoing research to exploit the information contained in the contexts in which different instances of an expression could occur. This information is used to investigate the question of whether an expression is literal or MWE. Lexical and syntactic context features derived from vector representations are shown to be more effective over traditional statistical measures to identify tokens of MWEs.

2016

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Classifying Out-of-vocabulary Terms in a Domain-Specific Social Media Corpus
SoHyun Park | Afsaneh Fazly | Annie Lee | Brandon Seibel | Wenjie Zi | Paul Cook
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper we consider the problem of out-of-vocabulary term classification in web forum text from the automotive domain. We develop a set of nine domain- and application-specific categories for out-of-vocabulary terms. We then propose a supervised approach to classify out-of-vocabulary terms according to these categories, drawing on features based on word embeddings, and linguistic knowledge of common properties of out-of-vocabulary terms. We show that the features based on word embeddings are particularly informative for this task. The categories that we predict could serve as a preliminary, automatically-generated source of lexical knowledge about out-of-vocabulary terms. Furthermore, we show that this approach can be adapted to give a semi-automated method for identifying out-of-vocabulary terms of a particular category, automotive named entities, that is of particular interest to us.

2014

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Learning Verb Classes in an Incremental Model
Libby Barak | Afsaneh Fazly | Suzanne Stevenson
Proceedings of the Fifth Workshop on Cognitive Modeling and Computational Linguistics

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A Usage-Based Model of Early Grammatical Development
Barend Beekhuizen | Rens Bod | Afsaneh Fazly | Suzanne Stevenson | Arie Verhagen
Proceedings of the Fifth Workshop on Cognitive Modeling and Computational Linguistics

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A Cognitive Model of Semantic Network Learning
Aida Nematzadeh | Afsaneh Fazly | Suzanne Stevenson
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Acquisition of Desires before Beliefs: A Computional Investigation
Libby Barak | Afsaneh Fazly | Suzanne Stevenson
Proceedings of the Seventeenth Conference on Computational Natural Language Learning

2012

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Unsupervised Disambiguation of Image Captions
Wesley May | Sanja Fidler | Afsaneh Fazly | Sven Dickinson | Suzanne Stevenson
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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Modeling the Acquisition of Mental State Verbs
Libby Barak | Afsaneh Fazly | Suzanne Stevenson
Proceedings of the 3rd Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2012)

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A Computational Model of Memory, Attention, and Word Learning
Aida Nematzadeh | Afsaneh Fazly | Suzanne Stevenson
Proceedings of the 3rd Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2012)

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Using Noun Similarity to Adapt an Acceptability Measure for Persian Light Verb Constructions
Shiva Taslimipoor | Afsaneh Fazly | Ali Hamzeh
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Light verb constructions (LVCs), such as take a walk and make a decision, are a common subclass of multiword expressions (MWEs), whose distinct syntactic and semantic properties call for a special treatment within a computational system. In particular, LVCs are formed semi-productively: often a semantically-general verb (such as take) combines with a number of semantically-similar nouns to form semantically-related LVCs, as in make a decision/choice/commitment. Nonetheless, there are restrictions as to which verbs combine with which class of nouns. A proper computational account of LVCs is even more important for languages such as Persian, in which most verbs are of the form of LVCs. Recently, there has been some work on the automatic identification of MWEs (including LVCs) in resource-rich languages, such as English and Dutch. We adapt such existing techniques for the automatic identification of LVCs in Persian, an under-resourced language. Specifically, we extend an existing statistical measure of the acceptability of English LVCs (Fazly et al., 2007) to make explicit use of semantic classes of noun, and show that such classes are in particular useful for determining the LVC acceptability of new combinations.

2009

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Unsupervised Type and Token Identification of Idiomatic Expressions
Afsaneh Fazly | Paul Cook | Suzanne Stevenson
Computational Linguistics, Volume 35, Number 1, March 2009

2008

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Fast Mapping in Word Learning: What Probabilities Tell Us
Afra Alishahi | Afsaneh Fazly | Suzanne Stevenson
CoNLL 2008: Proceedings of the Twelfth Conference on Computational Natural Language Learning

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An Incremental Bayesian Model for Learning Syntactic Categories
Christopher Parisien | Afsaneh Fazly | Suzanne Stevenson
CoNLL 2008: Proceedings of the Twelfth Conference on Computational Natural Language Learning

2007

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Distinguishing Subtypes of Multiword Expressions Using Linguistically-Motivated Statistical Measures
Afsaneh Fazly | Suzanne Stevenson
Proceedings of the Workshop on A Broader Perspective on Multiword Expressions

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Pulling their Weight: Exploiting Syntactic Forms for the Automatic Identification of Idiomatic Expressions in Context
Paul Cook | Afsaneh Fazly | Suzanne Stevenson
Proceedings of the Workshop on A Broader Perspective on Multiword Expressions

2006

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Automatically Constructing a Lexicon of Verb Phrase Idiomatic Combinations
Afsaneh Fazly | Suzanne Stevenson
11th Conference of the European Chapter of the Association for Computational Linguistics

2005

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Automatically Distinguishing Literal and Figurative Usages of Highly Polysemous Verbs
Afsaneh Fazly | Ryan North | Suzanne Stevenson
Proceedings of the ACL-SIGLEX Workshop on Deep Lexical Acquisition

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Automatic Acquisition of Knowledge About Multiword Predicates
Afsaneh Fazly | Suzanne Stevenson
Proceedings of the 19th Pacific Asia Conference on Language, Information and Computation

2004

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Statistical Measures of the Semi-Productivity of Light Verb Constructions
Suzanne Stevenson | Afsaneh Fazly | Ryan North
Proceedings of the Workshop on Multiword Expressions: Integrating Processing

2003

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Testing the Efficacy of Part-of-Speech Information in Word Completion
Afsaneh Fazly | Graeme Hirst
Proceedings of the 2003 EACL Workshop on Language Modeling for Text Entry Methods