mlsanityData to 14 Oct 2026

SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models

arXiv 2210.11670 ↗First posted 21 Oct 2022cs.CLcited by 1

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Abstract (arXiv)

This paper describes the Stevens Institute of Technology's submission for the WMT 2022 Shared Task: Code-mixed Machine Translation (MixMT). The task consisted of two subtasks, subtask 1 Hindi/English to Hinglish and subtask 2 Hinglish to English translation. Our findings lie in the improvements made through the use of large pre-trained multilingual NMT models and in-domain datasets, as well as back-translation and ensemble techniques. The translation output is automatically evaluated against the reference translations using ROUGE-L and WER. Our system achieves the 1st position on subtask 2 according to ROUGE-L, WER, and human evaluation, 1st position on subtask 1 according to WER and human evaluation, and 3rd position on subtask 1 with respect to ROUGE-L metric.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 28 of its 47 references that are in the map. First posted on arXiv 21 Oct 2022.

Builds on

28 of 47 references.

References · 28

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16 more
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Citing papers per year: 2025: 1.

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  1. 2025Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models across Modalitiescited by 5