Semantically Informed Slang Interpretation

Zhewei Sun, Richard Zemel, Yang Xu


Abstract
Slang is a predominant form of informal language making flexible and extended use of words that is notoriously hard for natural language processing systems to interpret. Existing approaches to slang interpretation tend to rely on context but ignore semantic extensions common in slang word usage. We propose a semantically informed slang interpretation (SSI) framework that considers jointly the contextual and semantic appropriateness of a candidate interpretation for a query slang. We perform rigorous evaluation on two large-scale online slang dictionaries and show that our approach not only achieves state-of-the-art accuracy for slang interpretation in English, but also does so in zero-shot and few-shot scenarios where training data is sparse. Furthermore, we show how the same framework can be applied to enhancing machine translation of slang from English to other languages. Our work creates opportunities for the automated interpretation and translation of informal language.
Anthology ID:
2022.naacl-main.383
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5213–5231
Language:
URL:
https://aclanthology.org/2022.naacl-main.383
DOI:
10.18653/v1/2022.naacl-main.383
Bibkey:
Cite (ACL):
Zhewei Sun, Richard Zemel, and Yang Xu. 2022. Semantically Informed Slang Interpretation. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5213–5231, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Semantically Informed Slang Interpretation (Sun et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.383.pdf
Code
 zhewei-sun/slanginterp