Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings

Arun Ramachandran, Gerard de Melo


Abstract
Emotion lexicons provide information about associations between words and emotions. They have proven useful in analyses of reviews, literary texts, and posts on social media, among other things. We evaluate the feasibility of deriving emotion lexicons cross-lingually, especially for low-resource languages, from existing emotion lexicons in resource-rich languages. For this, we start out from very small corpora to induce cross-lingually aligned vector spaces. Our study empirically analyses the effectiveness of the induced emotion lexicons by measuring translation precision and correlations with existing emotion lexicons, along with measurements on a downstream task of sentence emotion prediction.
Anthology ID:
2020.coling-main.517
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5879–5890
Language:
URL:
https://aclanthology.org/2020.coling-main.517
DOI:
10.18653/v1/2020.coling-main.517
Bibkey:
Cite (ACL):
Arun Ramachandran and Gerard de Melo. 2020. Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5879–5890, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings (Ramachandran & de Melo, COLING 2020)
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PDF:
https://aclanthology.org/2020.coling-main.517.pdf