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Improving cross-lingual representation for semantic retrieval with code-switching
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DOI：10.1016/j.knosys.2025.113919.png)
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Abstract
En 中文
Semantic Retrieval (SR) has become an indispensable part of the FAQ system in the task-oriented question-answering (QA) dialogue scenario. The demand for a cross-lingual smart customer service system for e-commerce platforms and specific business scenarios has been increasing recently Most previous studies directly exploit cross-lingual pre-trained models (PTMs) for multilingual knowledge retrieval, while some also incorporate continual pre-training before fine-tuning PTMs on downstream tasks. However, no matter which schema is used, the previous work ignores to inform PTMs of some features of the downstream task, i.e. train their PTMs without providing any signals related to the downstream task (e.g., SR). To this end, in this work, we propose an Alternative Cross-lingual PTM for SR via code-switching. We are the first to utilize the code-switching approach for cross-lingual SR. Besides, we introduce the novel code-switched continual pre-training instead of directly using the PTMs on the SR tasks. The experimental results show that our proposed approach consistently outperforms the previous SOTA methods on SR and semantic textual similarity (STS) tasks with three business corpora and four open datasets in 20+ languages. Our approach outperforms the strongest baseline over 3.7 points on these datasets on average. The code is available at https://github.com/miradel51/codemix_ptm .
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