Vanlalmuansangi Khenglawt


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Findings of the WMT 2023 Shared Task on Low-Resource Indic Language Translation
Santanu Pal | Partha Pakray | Sahinur Rahman Laskar | Lenin Laitonjam | Vanlalmuansangi Khenglawt | Sunita Warjri | Pankaj Kundan Dadure | Sandeep Kumar Dash
Proceedings of the Eighth Conference on Machine Translation

This paper presents the results of the low-resource Indic language translation task organized alongside the Eighth Conference on Machine Translation (WMT) 2023. In this task, participants were asked to build machine translation systems for any of four language pairs, namely, English-Assamese, English-Mizo, English-Khasi, and English-Manipuri. For this task, the IndicNE-Corp1.0 dataset is released, which consists of parallel and monolingual corpora for northeastern Indic languages such as Assamese, Mizo, Khasi, and Manipuri. The evaluation will be carried out using automatic evaluation metrics (BLEU, TER, RIBES, COMET, ChrF) and human evaluation.


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Language Resource Building and English-to-Mizo Neural Machine Translation Encountering Tonal Words
Vanlalmuansangi Khenglawt | Sahinur Rahman Laskar | Santanu Pal | Partha Pakray | Ajoy Kumar Khan
Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference

Multilingual country like India has an enormous linguistic diversity and has an increasing demand towards developing language resources such that it will outreach in various natural language processing applications like machine translation. Low-resource language translation possesses challenges in the field of machine translation. The challenges include the availability of corpus and differences in linguistic information. This paper investigates a low-resource language pair, English-to-Mizo exploring neural machine translation by contributing an Indian language resource, i.e., English-Mizo corpus. In this work, we explore one of the main challenges to tackling tonal words existing in the Mizo language, as they add to the complexity on top of low-resource challenges for any natural language processing task. Our approach improves translation accuracy by encountering tonal words of Mizo and achieved a state-of-the-art result in English-to-Mizo translation.