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

• URL: http://arxiv.org/abs/2210.12540v1
• Date: Sat, 22 Oct 2022 20:05:40 GMT
• Title: EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching
• Authors: Chenxi Whitehouse, Fenia Christopoulou, Ignacio Iacobacci
• Abstract summary: Code-Switching offers language alignment at word- or phrase-level. Existing approaches either use dictionaries or parallel sentences with word-alignment to generate CS data. We propose EntityCS to capture fine-grained cross-lingual semantics without corrupting syntax.
• Score: 15.884119564193924
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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