Papers › Zero-shot Sequence Labeling for Transformer-based Sentence Classifiers
Zero-shot Sequence Labeling for Transformer-based Sentence Classifiers
Kamil Bujel, Helen Yannakoudakis, Marek Rei
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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