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Attention Interpretability Across NLP Tasks

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arxiv 1909.11218 v1 pith:QZNJCTGS submitted 2019-09-24 cs.CL cs.LG

Shikhar Vashishth , Shyam Upadhyay , Gaurav Singh Tomar , Manaal Faruqui This is my paper · ORCID
classification cs.CLcs.LG
keywords attentioninterpretabilitymodelobservationstaskswhenacrossamid
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The attention layer in a neural network model provides insights into the model's reasoning behind its prediction, which are usually criticized for being opaque. Recently, seemingly contradictory viewpoints have emerged about the interpretability of attention weights (Jain & Wallace, 2019; Vig & Belinkov, 2019). Amid such confusion arises the need to understand attention mechanism more systematically. In this work, we attempt to fill this gap by giving a comprehensive explanation which justifies both kinds of observations (i.e., when is attention interpretable and when it is not). Through a series of experiments on diverse NLP tasks, we validate our observations and reinforce our claim of interpretability of attention through manual evaluation.

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Cited by 3 Pith papers

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    DAGCD improves context faithfulness by amplifying context tokens that attention marks as relevant, scaled by token-level uncertainty.

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  3. New Faithfulness-Centric Interpretability Paradigms for Natural Language Processing

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    The thesis shows that randomly masking input tokens during fine-tuning makes post-hoc explanations of NLP models consistently faithful under an erasure-based faithfulness metric.

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