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
Findings of the Covid-19 MLIA Machine Translation Task
Francisco Casacuberta, Alexandru Ceausu, Khalid Choukri, Miltos Deligiannis, Miguel Domingo, Mercedes García-Martínez, Manuel Herranz, Guillaume Jacquet, Vassilis Papavassiliou, Stelios Piperidis, Prokopis Prokopidis, Dimitris Roussis, Marwa Hadj Salah
Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation
Ke Wang, Xin Ge, Jiayi Wang, Yu Zhao, Yuqi Zhang
ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics
Chantal Amrhein, Nikita Moghe, Liane Guillou
The Effect of Normalization for Bi-directional Amharic-English Neural Machine Translation
Tadesse Destaw Belay, Atnafu Lambebo Tonja, Olga Kolesnikova, Seid Muhie Yimam, Abinew Ali Ayele, Silesh Bogale Haile, Grigori Sidorov, Alexander Gelbukh
Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport
Kelly Marchisio, Ali Saad-Eldin, Kevin Duh, Carey Priebe, Philipp Koehn
Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature
Katherine Thai, Marzena Karpinska, Kalpesh Krishna, Bill Ray, Moira Inghilleri, John Wieting, Mohit Iyyer