Zero-shot Sequence Labeling for Transformer-based Sentence Classifiers

Kamil Bujel; Helen Yannakoudakis; Marek Rei

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arXiv · 2103.14465

Zero-shot Sequence Labeling for Transformer-based Sentence Classifiers

Kamil Bujel·Helen Yannakoudakis·Marek Rei

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Abstract

We investigate how sentence-level transformers can be modified into effective sequence labelers at the token level without any direct supervision. Existing approaches to zero-shot sequence labeling do not perform well when applied on transformer-based architectures. As transformers contain multiple layers of multi-head self-attention, information in the sentence gets distributed between many tokens, negatively affecting zero-shot token-level performance. We find that a soft attention module which explicitly encourages sharpness of attention weights can significantly outperform existing methods.

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Kamil Bujel, Helen Yannakoudakis, Marek Rei. 2021-03-26. Zero-shot Sequence Labeling for Transformer-based Sentence Classifiers. https://arxiv.org/abs/2103.14465

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