Figurative Language in Recognizing Textual Entailment

Chakrabarty, Tuhin; Ghosh, Debanjan; Poliak, Adam; Muresan, Smaranda

doi:10.18653/v1/2021.findings-acl.297

Cited by 14 publications

(17 citation statements)

References 37 publications

(19 reference statements)

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Section: Discussionmentioning

confidence: 88%

Proceedings of the Workshop on Dimensions of Meaning: Distributional and Curated Semantics (DistCurate 2022)

2022

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Present-day language models provide contextual representations of words: a given element of the model's vocabulary will generally be represented differently depending on the larger (linguistic and/or non-linguistic) context in which it appears. This is undoubtedly a key feature of the success of these models. What do these contextual representations mean for linguists working on lexical semantics? In this talk, I'll argue, first and foremost, that contextual representations are better aligned with linguists' conception of word meaning than any previous representation scheme in NLP, including symbolic approaches. I will then describe some ways in which linguists can use large pretrained language models to gain new insights into lexical meaning, and some ways in which language model development could be fruitfully informed by findings in linguistics. The overall take-away is that the turn toward contextual representations has created an exciting new space for collaboration between linguists and NLPers.Bio: Christopher Potts is Professor and Chair of Linguistics and Professor (by courtesy) of Computer Science at Stanford, and a faculty member in the Stanford NLP Group and the Stanford AI Lab. His group uses computational methods to explore topics in emotion expression, context-dependent language use, systematicity and compositionality, and model interpretability. This research combines methods from linguistics, cognitive psychology, and computer science, in the service of both scientific discovery and technology development. He is the author of the 2005 book The Logic of Conventional Implicatures as well as numerous scholarly papers in computational and theoretical linguistics.

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Section: Discussionmentioning

confidence: 88%

Proceedings of the Workshop on Dimensions of Meaning: Distributional and Curated Semantics (DistCurate 2022)

2022

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“…Despite progress on automatic recognition of metaphors (e.g. Veale 2016, Shutova et al 2015, Chakrabarty et al 2021, the kind of detail shown here is generally not retrievable computationally. Lane Lawley and Lenhart Schubert generate"Logical Story Representations via FrameNet + Semantic Parsing".…”

Section: Introduction To the Workhopmentioning

confidence: 91%

Comparing Distributional and Curated Approaches for Cross-lingual Frame Alignment

Baker¹,

Ellsworth²,

Petruck³

et al. 2022

Proceedings of the Workshop on Dimensions of Meaning: Distributional and Curated Semantics (DistCurate 2022)

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Despite advances in statistical approaches to the modeling of meaning, many questions about the ideal way of exploiting both knowledge-based (e.g., FrameNet, WordNet) and data-based methods (e.g., BERT) remain unresolved. This workshop focuses on these questions with three session papers that run the gamut from highly distributional methods (Lekkas et al., 2022), to highly curated methods (Gamonal, 2022), and techniques with statistical methods producing structured semantics (Lawley and Schubert, 2022).In addition, we begin the workshop with a small comparison of cross-lingual techniques for frame semantic alignment for one language pair (Spanish and English). None of the distributional techniques consistently aligns the 1-best frame match from English to Spanish, all failing in at least one case. Predicting which techniques will align which frames crosslinguistically is not possible from any known characteristic of the alignment technique or the frames. Although distributional techniques are a rich source of semantic information for many tasks, at present curated, knowledge-based semantics remains the only technique that can consistently align frames across languages.

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“…Simile interpretation focused on inferring the implicit property (Qadir et al, 2016). In other lines of work, Chakrabarty et al (2020b) and Zhang et al (2021) proposed methods for generating similes from their literal counterparts, while Chakrabarty et al (2021a) showed that state-of-the-art NLI models fail on pragmatic inferences involving similes.…”

Section: Similesmentioning

confidence: 99%

It’s not Rocket Science: Interpreting Figurative Language in Narratives

Chakrabarty

Choi

Shwartz

2022

Transactions of the Association for Computational Linguistics

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Figurative language is ubiquitous in English. Yet, the vast majority of NLP research focuses on literal language. Existing text representations by design rely on compositionality, while figurative language is often non- compositional. In this paper, we study the interpretation of two non-compositional figurative languages (idioms and similes). We collected datasets of fictional narratives containing a figurative expression along with crowd-sourced plausible and implausible continuations relying on the correct interpretation of the expression. We then trained models to choose or generate the plausible continuation. Our experiments show that models based solely on pre-trained language models perform substantially worse than humans on these tasks. We additionally propose knowledge-enhanced models, adopting human strategies for interpreting figurative language types: inferring meaning from the context and relying on the constituent words’ literal meanings. The knowledge-enhanced models improve the performance on both the discriminative and generative tasks, further bridging the gap from human performance.

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Figurative Language in Recognizing Textual Entailment

Cited by 14 publications

References 37 publications

Proceedings of the Workshop on Dimensions of Meaning: Distributional and Curated Semantics (DistCurate 2022)

Proceedings of the Workshop on Dimensions of Meaning: Distributional and Curated Semantics (DistCurate 2022)

Comparing Distributional and Curated Approaches for Cross-lingual Frame Alignment

It’s not Rocket Science: Interpreting Figurative Language in Narratives

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