Adaptive Cross-lingual Text Classification through In-Context One-Shot Demonstrations

Emilio Cueva, Adrian Lopez Monroy, Fernando Sánchez-Vega, Thamar Solorio

Abstract
Zero-Shot Cross-lingual Transfer (ZS-XLT) utilizes a model trained in a source language to make predictions in another language, often with a performance loss. To alleviate this, additional improvements can be achieved through subsequent adaptation using examples in the target language. In this paper, we exploit In-Context Tuning (ICT) for One-Shot Cross-lingual transfer in the classification task by introducing In-Context Cross-lingual Transfer (IC-XLT). The novel concept involves training a model to learn from context examples and subsequently adapting it during inference to a target language by prepending a One-Shot context demonstration in that language. Our results show that IC-XLT successfully leverages target-language examples to improve the cross-lingual capabilities of the evaluated mT5 model, outperforming prompt-based models in the Zero and Few-shot scenarios adapted through fine-tuning. Moreover, we show that when source-language data is limited, the fine-tuning framework employed for IC-XLT performs comparably to prompt-based fine-tuning with significantly more training data in the source language.
Anthology ID:
2024.naacl-long.460
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8317–8335
Language:
URL:
https://aclanthology.org/2024.naacl-long.460
DOI:
10.18653/v1/2024.naacl-long.460
Bibkey:
Cite (ACL):
Emilio Cueva, Adrian Lopez Monroy, Fernando Sánchez-Vega, and Thamar Solorio. 2024. Adaptive Cross-lingual Text Classification through In-Context One-Shot Demonstrations. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 8317–8335, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Adaptive Cross-lingual Text Classification through In-Context One-Shot Demonstrations (Cueva et al., NAACL 2024)
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https://preview.aclanthology.org/nschneid-patch-4/2024.naacl-long.460.pdf
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