Semantics-Aware Inferential Network for Natural Language Understanding

Authors

  • Shuiliang Zhang Shanghai Jiao Tong University
  • Hai Zhao Shanghai Jiao Tong University
  • Junru Zhou Shanghai Jiao Tong University
  • Xi Zhou CloudWalk Technology
  • Xiang Zhou CloudWalk Technology

Keywords:

Question Answering

Abstract

For natural language understanding tasks, either machine reading comprehension or natural language inference, both semantics-aware and inference are favorable features of the concerned modeling for better understanding performance. Thus we propose a Semantics-Aware Inferential Network (SAIN) to meet such a motivation. Taking explicit contextualized semantics as a complementary input, the inferential module of SAIN enables a series of reasoning steps over semantic clues through an attention mechanism. By stringing these steps, the inferential network effectively learns to perform iterative reasoning which incorporates both explicit semantics and contextualized representations. In terms of well pre-trained language models as front-end encoder, our model achieves significant improvement on 11 tasks including machine reading comprehension and natural language inference.

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Published

2021-05-18

How to Cite

Zhang, S., Zhao, H., Zhou, J., Zhou, X., & Zhou, X. (2021). Semantics-Aware Inferential Network for Natural Language Understanding. Proceedings of the AAAI Conference on Artificial Intelligence, 35(16), 14437-14445. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17697

Issue

Section

AAAI Technical Track on Speech and Natural Language Processing III