Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and Definitions

Pinzhen Chen, Zheng Zhao


Abstract
This paper presents a winning submission to the SemEval 2022 Task 1 on two sub-tasks: reverse dictionary and definition modelling. We leverage a recently proposed unified model with multi-task training. It utilizes data symmetrically and learns to tackle both tracks concurrently. Analysis shows that our system performs consistently on diverse languages, and works the best with sgns embeddings. Yet, char and electra carry intriguing properties. The two tracks’ best results are always in differing subsets grouped by linguistic annotations. In this task, the quality of definition generation lags behind, and BLEU scores might be misleading.
Anthology ID:
2022.semeval-1.8
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
75–81
Language:
URL:
https://aclanthology.org/2022.semeval-1.8
DOI:
10.18653/v1/2022.semeval-1.8
Bibkey:
Cite (ACL):
Pinzhen Chen and Zheng Zhao. 2022. Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and Definitions. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 75–81, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and Definitions (Chen & Zhao, SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.8.pdf
Video:
 https://aclanthology.org/2022.semeval-1.8.mp4
Code
 pinzhenchen/unifiedrevdicdefmod