Self-Attention for Incomplete Utterance Rewriting

The architecture of our proposed model

Abstract

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.

Type
Publication
In 2022 IEEE International Conference on Acoustics, Speech and Signal Processing
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Yong Zhang
Yong Zhang
Researcher
Ning Cheng
Ning Cheng
Director