This study investigates machine translation between related languages i.e., languages within the same family that share linguistic characteristics such as word order and lexical similarity.Machine translation through few-shot prompting leverages a small set of translation pair examples to generate translations for test sentences.This procedure requires the model to learn how to generate translations while simultaneously ensuring that token ordering is maintained to produce a fluent and accurate translation.We propose that for related languages, the task of machine translation can be simplified by leveraging the monotonic alignment characteristic of such languages.We introduce DecoMT, a novel approach of fewshot prompting that decomposes the translation process into a sequence of word chunk translations.Through automatic and human evaluation conducted on multiple related language pairs across various language families, we demonstrate that our proposed approach of decomposed prompting surpasses multiple established few-shot baseline approaches.For example, DecoMT outperforms the strong fewshot prompting BLOOM model with an average improvement of 8 chrF++ scores across the examined languages.

Minh Nguyen, Nancy Chen. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

We report automatic speech recognition accuracy for individual words using eleven surface electromyographic (sEMG) recording locations on the face and neck during three speaking modes: vocalized, mouthed, and mentally rehearsed. An HMM based recognition system was trained and tested on a 65 word vocabulary produced by 9 American English speakers in all three speaking modes. Our results indicate high sEMG-based recognition accuracy for the vocalized and mouthed speaking modes (mean rates of 92.1 % and 86.7% respectively), but an inability to conduct recognition on mentally rehearsed speech due to a lack of sufficient sEMG activity. Index Terms: sEMG, subvocal speech, speech recognition