Self-Attention for Incomplete Utterance Rewriting

The architecture of our proposed model


Incomplete utterance rewriting (IUR) has recently become an essential task in NLP, aiming to complement the incomplete utterance with sufficient context information for comprehension. In this paper, we propose a novel method by directly extracting the coreference and omission relationship from the self-attention weight matrix of the transformer in-stead of word embeddings and edit the original text accordingly to generate the complete utterance. Benefiting from the rich information in the self-attention weight matrix, our method achieved competitive results on public IUR datasets.

In 2022 IEEE International Conference on Acoustics, Speech and Signal Processing
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Yong Zhang
Yong Zhang
Ning Cheng
Ning Cheng