Exploiting WordNet Synset and Hypernym Representations for Answer Selection

Weikang Li, Yunfang Wu


Abstract
Answer selection (AS) is an important subtask of document-based question answering (DQA). In this task, the candidate answers come from the same document, and each answer sentence is semantically related to the given question, which makes it more challenging to select the true answer. WordNet provides powerful knowledge about concepts and their semantic relations so we employ WordNet to enrich the abilities of paraphrasing and reasoning of the network-based question answering model. Specifically, we exploit the synset and hypernym concepts to enrich the word representation and incorporate the similarity scores of two concepts that share the synset or hypernym relations into the attention mechanism. The proposed WordNet-enhanced hierarchical model (WEHM) consists of four modules, including WordNet-enhanced word representation, sentence encoding, WordNet-enhanced attention mechanism, and hierarchical document encoding. Extensive experiments on the public WikiQA and SelQA datasets demonstrate that our proposed model significantly improves the baseline system and outperforms all existing state-of-the-art methods by a large margin.
Anthology ID:
2020.aacl-main.14
Volume:
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
Month:
December
Year:
2020
Address:
Suzhou, China
Editors:
Kam-Fai Wong, Kevin Knight, Hua Wu
Venue:
AACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
106–115
Language:
URL:
https://aclanthology.org/2020.aacl-main.14
DOI:
Bibkey:
Cite (ACL):
Weikang Li and Yunfang Wu. 2020. Exploiting WordNet Synset and Hypernym Representations for Answer Selection. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 106–115, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Exploiting WordNet Synset and Hypernym Representations for Answer Selection (Li & Wu, AACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.aacl-main.14.pdf
Data
WikiQA