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 instead 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.
Cite
@article{arxiv.2202.12160,
title = {Self-Attention for Incomplete Utterance Rewriting},
author = {Yong Zhang and Zhitao Li and Jianzong Wang and Ning Cheng and Jing Xiao},
journal= {arXiv preprint arXiv:2202.12160},
year = {2022}
}
Comments
Accepted by the 47th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2022)