English-Vietnamese Cross-lingual Semantic Textual Similarity using Sentence Transformer model

Khang Huu Nguyen, Dat Cong Dinh, Hang Thi-Thuy Le, Dien Dinh

Abstract: Cross-lingual Semantic Textual Similarity (STS) is a challenging problem in Natural Language Understanding tasks, especially for low-resource languages like Vietnamese. Currently, one of the state-of-the-art approaches for this problem is to use distilled multilingual Sentence Transformer model. However, there are few studies on how these models work for English-Vietnamese language pairs. In this paper, we aim to inspect the performance of these models in the English-Vietnamese STS tasks. From our findings, we will propose possible improvements for this approach in the future.