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
What do Large Language Models Need for Machine Translation Evaluation?
Shenbin Qian, Archchana Sindhujan, Minnie Kabra, Diptesh Kanojia, Constantin Orăsan, Tharindu Ranasinghe, Frédéric Blain
A Multi-task Learning Framework for Evaluating Machine Translation of Emotion-loaded User-generated Content
Shenbin Qian, Constantin Orăsan, Diptesh Kanojia, Félix do Carmo
Creative and Context-Aware Translation of East Asian Idioms with GPT-4
Kenan Tang, Peiyang Song, Yao Qin, Xifeng Yan
On the Implications of Verbose LLM Outputs: A Case Study in Translation Evaluation
Eleftheria Briakou, Zhongtao Liu, Colin Cherry, Markus Freitag
What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Study
Beatrice Savoldi, Sara Papi, Matteo Negri, Ana Guerberof, Luisa Bentivogli
Disentangling Singlish Discourse Particles with Task-Driven Representation
Linus Tze En Foo, Lynnette Hui Xian Ng
Is Preference Alignment Always the Best Option to Enhance LLM-Based Translation? An Empirical Analysis
Hippolyte Gisserot-Boukhlef, Ricardo Rei, Emmanuel Malherbe, Céline Hudelot, Pierre Colombo, Nuno M. Guerreiro
Contrastive Token Learning with Similarity Decay for Repetition Suppression in Machine Translation
Huangyu Dai, Ben Chen, Kaidi Chen, Ying Han, Zihan Liang, Wen Jiang
EuroLLM: Multilingual Language Models for Europe
Pedro Henrique Martins, Patrick Fernandes, João Alves, Nuno M. Guerreiro, Ricardo Rei, Duarte M. Alves, José Pombal, Amin Farajian, Manuel Faysse, Mateusz Klimaszewski, Pierre Colombo, Barry Haddow, José G. C. de Souza, Alexandra Birch, André F. T. Martins
Machine Translation Advancements of Low-Resource Indian Languages by Transfer Learning
Bin Wei, Jiawei Zhen, Zongyao Li, Zhanglin Wu, Daimeng Wei, Jiaxin Guo, Zhiqiang Rao, Shaojun Li, Yuanchang Luo, Hengchao Shang, Jinlong Yang, Yuhao Xie, Hao Yang
HW-TSC's Submission to the CCMT 2024 Machine Translation Tasks
Zhanglin Wu, Yuanchang Luo, Daimeng Wei, Jiawei Zheng, Bin Wei, Zongyao Li, Hengchao Shang, Jiaxin Guo, Shaojun Li, Weidong Zhang, Ning Xie, Hao Yang
Choose the Final Translation from NMT and LLM hypotheses Using MBR Decoding: HW-TSC's Submission to the WMT24 General MT Shared Task
Zhanglin Wu, Daimeng Wei, Zongyao Li, Hengchao Shang, Jiaxin Guo, Shaojun Li, Zhiqiang Rao, Yuanchang Luo, Ning Xie, Hao Yang
Task Arithmetic for Language Expansion in Speech Translation
Yao-Fei Cheng, Hayato Futami, Yosuke Kashiwagi, Emiru Tsunoo, Wen Shen Teo, Siddhant Arora, Shinji Watanabe
GOSt-MT: A Knowledge Graph for Occupation-related Gender Biases in Machine Translation
Orfeas Menis Mastromichalakis, Giorgos Filandrianos, Eva Tsouparopoulou, Dimitris Parsanoglou, Maria Symeonaki, Giorgos Stamou