A Unified Model for Reverse Dictionary and Definition Modelling

Pinzhen Chen, Zheng Zhao


Abstract
We build a dual-way neural dictionary to retrieve words given definitions, and produce definitions for queried words. The model learns the two tasks simultaneously and handles unknown words via embeddings. It casts a word or a definition to the same representation space through a shared layer, then generates the other form in a multi-task fashion. Our method achieves promising automatic scores on previous benchmarks without extra resources. Human annotators prefer the model’s outputs in both reference-less and reference-based evaluation, indicating its practicality. Analysis suggests that multiple objectives benefit learning.
Anthology ID:
2022.aacl-short.2
Volume:
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
November
Year:
2022
Address:
Online only
Editors:
Yulan He, Heng Ji, Sujian Li, Yang Liu, Chua-Hui Chang
Venues:
AACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8–13
Language:
URL:
https://aclanthology.org/2022.aacl-short.2
DOI:
Bibkey:
Cite (ACL):
Pinzhen Chen and Zheng Zhao. 2022. A Unified Model for Reverse Dictionary and Definition Modelling. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 8–13, Online only. Association for Computational Linguistics.
Cite (Informal):
A Unified Model for Reverse Dictionary and Definition Modelling (Chen & Zhao, AACL-IJCNLP 2022)
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PDF:
https://aclanthology.org/2022.aacl-short.2.pdf