Multilingual Sentence-T5: Scalable Sentence Encoders for Multilingual Applications

Chihiro Yano, Akihiko Fukuchi, Shoko Fukasawa, Hideyuki Tachibana, Yotaro Watanabe

Abstract
Prior work on multilingual sentence embedding has demonstrated that the efficient use of natural language inference (NLI) data to build high-performance models can outperform conventional methods. However, the potential benefits from the recent “exponential” growth of language models with billions of parameters have not yet been fully explored. In this paper, we introduce Multilingual Sentence T5 (m-ST5), as a larger model of NLI-based multilingual sentence embedding, by extending Sentence T5, an existing monolingual model. By employing the low-rank adaptation (LoRA) technique, we have achieved a successful scaling of the model’s size to 5.7 billion parameters. We conducted experiments to evaluate the performance of sentence embedding and verified that the method outperforms the NLI-based prior approach. Furthermore, we also have confirmed a positive correlation between the size of the model and its performance. It was particularly noteworthy that languages with fewer resources or those with less linguistic similarity to English benefited more from the parameter increase. Our model is available at https://huggingface.co/pkshatech/m-ST5.
Anthology ID:
2024.lrec-main.1034
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
11849–11858
Language:
URL:
https://aclanthology.org/2024.lrec-main.1034
DOI:
Bibkey:
Cite (ACL):
Chihiro Yano, Akihiko Fukuchi, Shoko Fukasawa, Hideyuki Tachibana, and Yotaro Watanabe. 2024. Multilingual Sentence-T5: Scalable Sentence Encoders for Multilingual Applications. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 11849–11858, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Multilingual Sentence-T5: Scalable Sentence Encoders for Multilingual Applications (Yano et al., LREC-COLING 2024)
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https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1034.pdf
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