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

Chenxi Whitehouse; Fenia Christopoulou; Ignacio Iacobacci

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arXiv · 2210.12540

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

Chenxi Whitehouse·Fenia Christopoulou·Ignacio Iacobacci

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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 the 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 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.

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Chenxi Whitehouse, Fenia Christopoulou, Ignacio Iacobacci. 2023-02-13. EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching. https://arxiv.org/abs/2210.12540

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