Papers with MT research
A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models (2024.lrec-main)
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Chenyang Lyu, Zefeng Du, Jitao Xu, Yitao Duan, Minghao Wu, Teresa Lynn, Alham Fikri Aji, Derek F. Wong, Longyue Wang
| Challenge: | Large Language Models (LLMs) are introducing a new phase in machine translation . despite advances in MT, there are still many challenges to overcome . |
| Approach: | They propose to highlight several new directions for MT that are influenced by Large Language Models like GPT-4 and ChatGPT. |
| Outcome: | The proposed models offer vast linguistic understandings and bring innovative methodologies, such as prompt-based techniques, that have the potential to further elevate MT. |
MT-RewardTree: A Comprehensive Framework for Advancing LLM-Based Machine Translation via Reward Modeling (2025.findings-emnlp)
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| Challenge: | MT-RewardTree provides a framework for constructing, evaluating, and deploying process reward models in machine translation (MT) |
| Approach: | They propose a method for automatically generating token-level preference pairs using approximate Monte Carlo Tree Search. |
| Outcome: | The proposed framework achieves state-of-the-art performance in token-level evaluation and sequence-level analysis. |
Toward Machine Translation Literacy: How Lay Users Perceive and Rely on Imperfect Translations (2025.emnlp-main)
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Yimin Xiao, Yongle Zhang, Dayeon Ki, Calvin Bao, Marianna J. Martindale, Charlotte Vaughn, Ge Gao, Marine Carpuat
| Challenge: | Using machine translation tools for everyday tasks is becoming more commonplace, but a lack of evaluation strategies and alternatives can cause users to over-rely on it. |
| Approach: | They propose to use MT evaluation techniques to promote MT quality and MT literacy among its users. |
| Outcome: | The findings highlight the need for evaluation and NLP explanation techniques to promote MT quality and MT literacy among its users. |
Translating Domain-Specific Terminology in Typologically-Diverse Languages: A Study in Tax and Financial Education (2025.emnlp-main)
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| Challenge: | Existing public terminology datasets for MT research are limited in language coverage or domain specificity, making it difficult to assess or improve MT systems in specialized settings. |
| Approach: | They propose a multilingual terminology resource for tax and financial education covering seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole. |
| Outcome: | The proposed terminology resource covers seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole. |