Machine Translation
Machine translation (MT) aims to automatically translate text between languages, with current research heavily focused on leveraging large language models (LLMs) and exploring various architectures like encoder-decoder and decoder-only models. Key areas of investigation include improving translation quality, particularly for low-resource languages and specialized domains like medicine, mitigating biases (e.g., gender bias), and enhancing evaluation methods beyond simple correlation with human judgments. These advancements have significant implications for cross-cultural communication, information access, and the development of more equitable and effective multilingual technologies.
Papers
Navigating the Metrics Maze: Reconciling Score Magnitudes and Accuracies
Tom Kocmi, Vilém Zouhar, Christian Federmann, Matt Post
Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translation
Xu Huang, Zhirui Zhang, Xiang Geng, Yichao Du, Jiajun Chen, Shujian Huang
An approach for mistranslation removal from popular dataset for Indic MT Task
Sudhansu Bala Das, Leo Raphael Rodrigues, Tapas Kumar Mishra, Bidyut Kr. Patra
Tuning LLMs with Contrastive Alignment Instructions for Machine Translation in Unseen, Low-resource Languages
Zhuoyuan Mao, Yen Yu
A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism
Brian Thompson, Mehak Preet Dhaliwal, Peter Frisch, Tobias Domhan, Marcello Federico
POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation
Shilong Pan, Zhiliang Tian, Liang Ding, Zhen Huang, Zhihua Wen, Dongsheng Li
Convergences and Divergences between Automatic Assessment and Human Evaluation: Insights from Comparing ChatGPT-Generated Translation and Neural Machine Translation
Zhaokun Jiang, Qianxi Lv, Ziyin Zhang, Lei Lei
Aligning Translation-Specific Understanding to General Understanding in Large Language Models
Yichong Huang, Baohang Li, Xiaocheng Feng, Chengpeng Fu, Wenshuai Huo, Ting Liu, Bing Qin
Whose wife is it anyway? Assessing bias against same-gender relationships in machine translation
Ian Stewart, Rada Mihalcea
An Empirical study of Unsupervised Neural Machine Translation: analyzing NMT output, model's behavior and sentences' contribution
Isidora Chara Tourni, Derry Wijaya
Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies
Anaelia Ovalle, Ninareh Mehrabi, Palash Goyal, Jwala Dhamala, Kai-Wei Chang, Richard Zemel, Aram Galstyan, Yuval Pinter, Rahul Gupta
Improving Neural Machine Translation by Multi-Knowledge Integration with Prompting
Ke Wang, Jun Xie, Yuqi Zhang, Yu Zhao
First Attempt at Building Parallel Corpora for Machine Translation of Northeast India's Very Low-Resource Languages
Atnafu Lambebo Tonja, Melkamu Mersha, Ananya Kalita, Olga Kolesnikova, Jugal Kalita