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Article Dans Une Revue EMNLP Empirical Methods in Natural Language Processing Année : 2022

Acceptability Judgements via Examining the Topology of Attention Maps

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The role of the attention mechanism in encoding linguistic knowledge has received special interest in NLP. However, the attention heads' ability to judge the grammatical acceptability of a sentence has been underexplored. This paper approaches the paradigm of acceptability judgments with topological data analysis (TDA), showing that the topological properties of the attention graph can be efficiently exploited for two standard practices in linguistics: binary judgments and linguistic minimal pairs. Topological features enhance the BERTbased acceptability classifier scores by up to 0.24 Matthew's correlation coefficient score on COLA in three languages (English, Italian, and Swedish). By revealing the topological discrepancy between attention graphs of minimal pairs, we achieve the human-level performance on the BLIMP benchmark, outperforming nine statistical and Transformer LM baselines. At the same time, TDA provides the foundation for analyzing the linguistic functions of attention heads and interpreting the correspondence between the graph features and grammatical phenomena. We publicly release the code and other materials used in the experiments 1. * Equal contribution. 1 github.com/danchern97/tda4la

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hal-04019907 , version 1 (08-03-2023)

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Daniil Cherniavskii, Eduard Tulchinskii, Vladislav Mikhailov, Irina Proskurina, Laida Kushnareva, et al.. Acceptability Judgements via Examining the Topology of Attention Maps. EMNLP Empirical Methods in Natural Language Processing, 2022, Findings of the Association for Computational Linguistics: EMNLP 2022, pp.88-107. ⟨10.18653/v1/2022.findings-emnlp.7⟩. ⟨hal-04019907⟩
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