Leveraging Closed-Access Multilingual Embedding for Automatic Sentence Alignment in Low Resource Languages

• URL: http://arxiv.org/abs/2311.12179v1
• Date: Mon, 20 Nov 2023 20:48:25 GMT
• Title: Leveraging Closed-Access Multilingual Embedding for Automatic Sentence Alignment in Low Resource Languages
• Authors: Idris Abdulmumin and Auwal Abubakar Khalid and Shamsuddeen Hassan Muhammad and Ibrahim Said Ahmad and Lukman Jibril Aliyu and Babangida Sani and Bala Mairiga Abduljalil and Sani Ahmad Hassan
• Abstract summary: We present a simple but qualitative parallel sentence aligner that carefully leveraged the closed-access Cohere multilingual embedding. The proposed approach achieved $94.96$ and $54.83$ f1 scores on FLORES and MAFAND-MT, compared to $3.64$ and $0.64$ of LASER respectively. Our method also achieved an improvement of more than 5 BLEU scores over LASER, when the resulting datasets were used with MAFAND-MT to train translation models.
• Score: 2.4023321876826462
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The importance of qualitative parallel data in machine translation has long been determined but it has always been very difficult to obtain such in sufficient quantity for the majority of world languages, mainly because of the associated cost and also the lack of accessibility to these languages. Despite the potential for obtaining parallel datasets from online articles using automatic approaches, forensic investigations have found a lot of quality-related issues such as misalignment, and wrong language codes. In this work, we present a simple but qualitative parallel sentence aligner that carefully leveraged the closed-access Cohere multilingual embedding, a solution that ranked second in the just concluded #CoHereAIHack 2023 Challenge (see https://ai6lagos.devpost.com). The proposed approach achieved $94.96$ and $54.83$ f1 scores on FLORES and MAFAND-MT, compared to $3.64$ and $0.64$ of LASER respectively. Our method also achieved an improvement of more than 5 BLEU scores over LASER, when the resulting datasets were used with MAFAND-MT dataset to train translation models. Our code and data are available for research purposes here (https://github.com/abumafrim/Cohere-Align).

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