Few-Shot Cross-Lingual Learning for Event Detection
Luis Guzman Nateras, Viet Lai, Franck Dernoncourt, Thien Nguyen
Abstract
Cross-Lingual Event Detection (CLED) models are capable of performing the Event Detection (ED) task in multiple languages. Such models are trained using data from a source language and then evaluated on data from a distinct target language. Training is usually performed in the standard supervised setting with labeled data available in the source language. The Few-Shot Learning (FSL) paradigm is yet to be explored for CLED despite its inherent advantage of allowing models to better generalize to unseen event types. As such, in this work, we study the CLED task under an FSL setting. Our contribution is threefold: first, we introduce a novel FSL classification method based on Optimal Transport (OT); second, we present a novel regularization term to incorporate the global distance between the support and query sets; and third, we adapt our approach to the cross-lingual setting by exploiting the alignment between source and target data. Our experiments on three, syntactically-different, target languages show the applicability of our approach and its effectiveness at improving the cross-lingual performance of few-shot models for event detection.- Anthology ID:
- 2022.mrl-1.2
- Volume:
- Proceedings of the The 2nd Workshop on Multi-lingual Representation Learning (MRL)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Duygu Ataman, Hila Gonen, Sebastian Ruder, Orhan Firat, Gözde Gül Sahin, Jamshidbek Mirzakhalov
- Venue:
- MRL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 16–27
- Language:
- URL:
- https://aclanthology.org/2022.mrl-1.2
- DOI:
- 10.18653/v1/2022.mrl-1.2
- Cite (ACL):
- Luis Guzman Nateras, Viet Lai, Franck Dernoncourt, and Thien Nguyen. 2022. Few-Shot Cross-Lingual Learning for Event Detection. In Proceedings of the The 2nd Workshop on Multi-lingual Representation Learning (MRL), pages 16–27, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Few-Shot Cross-Lingual Learning for Event Detection (Guzman Nateras et al., MRL 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.mrl-1.2.pdf