Multi-Level Head-Wise Match and Aggregation in Transformer for Textual Sequence Matching
Abstract
Transformer has been successfully applied to many natural language processing tasks. However, for textual sequence matching, simple matching between the representation of a pair of sequences might bring in unnecessary noise. In this paper, we propose a new approach to sequence pair matching with Transformer, by learning head-wise matching representations on multiple levels. Experiments show that our proposed approach can achieve new state-of-the-art performance on multiple tasks that rely only on pre-computed sequence-vector-representation, such as SNLI, MNLI-match, MNLI-mismatch, QQP, and SQuAD-binary.
Cite
Text
Wang et al. "Multi-Level Head-Wise Match and Aggregation in Transformer for Textual Sequence Matching." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I05.6458Markdown
[Wang et al. "Multi-Level Head-Wise Match and Aggregation in Transformer for Textual Sequence Matching." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/wang2020aaai-multi-c/) doi:10.1609/AAAI.V34I05.6458BibTeX
@inproceedings{wang2020aaai-multi-c,
title = {{Multi-Level Head-Wise Match and Aggregation in Transformer for Textual Sequence Matching}},
author = {Wang, Shuohang and Lan, Yunshi and Tay, Yi and Jiang, Jing and Liu, Jingjing},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2020},
pages = {9209-9216},
doi = {10.1609/AAAI.V34I05.6458},
url = {https://mlanthology.org/aaai/2020/wang2020aaai-multi-c/}
}