EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching

Chenxi Whitehouse, Fenia Christopoulou, Ignacio Iacobacci

Abstract
Accurate alignment between languages is fundamental for improving cross-lingual pre-trained language models (XLMs). Motivated by the natural phenomenon of code-switching (CS) in multilingual speakers, CS has been used as an effective data augmentation method that offers language alignment at word- or phrase-level, in contrast to sentence-level via parallel instances. Existing approaches either use dictionaries or parallel sentences with word-alignment to generate CS data by randomly switching words in a sentence. However, such methods can be suboptimal as dictionaries disregard semantics, and syntax might become invalid after random word switching. In this work, we propose EntityCS, a method that focuses on Entity-level Code-Switching to capture fine-grained cross-lingual semantics without corrupting syntax. We use Wikidata and the English Wikipedia to construct an entity-centric CS corpus by switching entities to their counterparts in other languages. We further propose entity-oriented masking strategies during intermediate model training on the EntityCS corpus for improving entity prediction. Evaluation of the trained models on four entity-centric downstream tasks shows consistent improvements over the baseline with a notable increase of 10% in Fact Retrieval. We release the corpus and models to assist research on code-switching and enriching XLMs with external knowledge.
Anthology ID:
2022.findings-emnlp.499
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6698–6714
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.499
DOI:
Bibkey:
Cite (ACL):
Chenxi Whitehouse, Fenia Christopoulou, and Ignacio Iacobacci. 2022. EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6698–6714, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching (Whitehouse et al., Findings 2022)
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