Context-Aware MT

MT: Machine Translation

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15 papers4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

Are These Programmers Ripe Enough to Eat? Evaluating How LLMs Handle Lexical Knowledge Conflicts in Translation
Nov 6, 2026Dongxu Wu, Sara Court, Micha ElsnerKnowledge Conflicts in Language Models Machine Translation
The Ohio State University
CULTURE-CMT: Benchmarking Contextualized Social Media Translation
Nov 1, 2026Linjuan Wu, Dingming Li, Yiwen Qiu +5Machine Translation Context-Aware MT
Xiaohongshu Inc. · Zhejiang University
Beyond Words Alone: Disentangling Distributed Evidence for Visually Grounded Cultural Translation
Oct 26, 2026Dingming Li, Yiwen Qiu, Linjuan Wu +8Machine Translation Multimodal MT
Zhejiang University · Xiaohongshu Inc.
Training with Ciphers to Enhance In-Context Terminology Learning For Translation from Rare Languages
Oct 17, 2026Deevyanshu Malu, Deeptanshu Malu, Sunita SarawagiMachine Translation In-Context Learning
IIT Bombay
alkhder at AlexandriaX-2026: Context-Aware English-to-Dialectal Arabic Dialogue Translation with QLoRA-Fine-Tuned NileChat-3B
Oct 15, 2026Hasan Alkhder, Maher Pasa, Maria Hanini +2Dialectal MT Machine Translation
Sakarya University, Türkiye · Altınbaş University, Türkiye · Imperial College London +1
Scaling Attention Head Analysis via Gradient-Based Attribution in Context-Aware Machine Translation
Sep 23, 2026Paweł Mąka, Yusuf Can Semerci, Jan Scholtes +1Gradient-Based Attribution Attention Head Analysis
Department of Advanced Computing Sciences Maastricht University
AlexandriaX 2026: The First Shared Task on Dialectal Arabic Machine Translation
Sep 19, 2026Abdellah El Mekki, AbdelRahim A. Elmadany, Samar M. Magdy +6Context-Aware MT Dialectal MT
The University of British Columbia · King Fahd University of Petroleum and Minerals · Lancaster University +4
Rosetta at AlexandriaX-2026: LoRA-Adapted NileChat for Context-Aware Dialectal Arabic Dialogue Translation
Sep 9, 2026Nada Esmaeil, Fathima Rena, Sibi Subhash +4Arabic NLP Low-Resource MT
Tanta University, Egypt · Alliance University, India · Robusta Studio, Egypt +2
Discourse Dependency: A Continuous Criterion for Translation Difficulty
Sep 4, 2026Ahrii Kim, Chanjun Park, Seong-heum KimMT Evaluation Context-Aware MT
AI-Bio Convergence Research Inst. Soongsil University · School of Software Soongsil University · Dept. of Intelligent Semiconductors Soongsil University
When to Use Extra Context: Evidence-Grounded Terminology Adaptation for Simultaneous Speech Translation
Jul 20, 2026Zeyu Yang, Satoshi NakamuraSpeech Translation Simultaneous Speech Translation
The Chinese University of Hong Kong, Shenzhen
Which Tokens Need Context? A Reference-Based Analysis of Translation Responsibility Using Fertility and Entropy
Jun 28, 2026Ramakrishna Appicharla, Baban Gain, Santanu Pal +1Context-Aware MT Machine Translation
Department of Computer Science and Engineering, Indian Institute of Technology Patna, India · 2Wipro AI Lab45, Bengaluru, India
Multilingual Reasoning Cascades Need More Context
Jun 25, 2026Arnav Mazumder, Dengjia Zhang, Shuyue Stella Li +2Cross-Lingual Reasoning in Language Models Context-Aware MT
University of Washington · Johns Hopkins University
Loong: A Human-Like Long Document Translation Agent with Observe-and-Act Adaptive Context Selection
May 28, 2026Yutong Wang, Xuebo Liu, Derek F. Wong +6Adaptive Retrieval Document-Level MT
Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen · 2NLP2CT Lab, Department of Computer and Information Science, University of Macau · 3Huawei Translation Services Center
Language-free Experience at Expo 2025 Osaka
May 1, 2026Michael Paul, Kenji Imamura, Xiaolin Wang +2Speech Translation Simultaneous Speech Translation
National Institute of Information and Communications Technology (NICT), Japan
Cross-Preference Learning for Sentence-Level and Context-Aware Machine Translation
Mar 26, 2026Ying Li, Xinglin Lyu, Junhui Li +5Context-Aware MT Machine Translation
School of Computer Science and Technology, Soochow University, Suzhou, China · School of Computer Science and Artificial Intelligence, Zhengzhou University, Zhengzhou, China · Jiangsu Key Lab of Language Computing, China +1

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Are These Programmers Ripe Enough to Eat? Evaluating How LLMs Handle Lexical Knowledge Conflicts in Translation

Dongxu Wu, Sara Court, Micha Elsner

The Ohio State University

Knowledge Conflicts in Language Models Machine Translation Context-Aware MT Framing Effects in Language Models

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External lexical resources can help large language models (LLMs) translate texts in low-resource languages and specialized domains, but these resources may conflict with models' prior lexical knowledge. To isolate this conflict, we construct a controlled Mandarin-to-English translation task in which familiar source words are assigned new meanings within and across semantic categories. We treat these prompt-supplied meanings as contextual knowledge (CK) and systematically evaluate the effects of word frequency, sentence context, and prompt framing on the translated outputs. Across nine models, CK reliance is greater for less frequent source words and in sentence contexts that support the supplied meanings. Prompt framing can reduce CK reliance, but seven of nine models favor CK even when explicitly instructed to ignore the supplied meanings. Within model families, larger models are more likely than smaller models to utilize CK in their outputs. Three follow-up experiments further show that modal weakening lowers CK reliance, the effect of repetition depends on prompt layout, and the effect of reasoning varies across models. Overall, our controlled study reveals how CK reliance varies across conditions and models, motivating new methods to detect and evaluate lexical conflicts and guide models' lexical choices in LLM-based translation.