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
Are Large Language Models State-of-the-art Quality Estimators for Machine Translation of User-generated Content?
Shenbin Qian, Constantin Orăsan, Diptesh Kanojia, Félix do Carmo
Edit Distances and Their Applications to Downstream Tasks in Research and Commercial Contexts
Félix do Carmo, Diptesh Kanojia
Translation Canvas: An Explainable Interface to Pinpoint and Analyze Translation Systems
Chinmay Dandekar, Wenda Xu, Xi Xu, Siqi Ouyang, Lei Li
Beyond Correlation: Interpretable Evaluation of Machine Translation Metrics
Stefano Perrella, Lorenzo Proietti, Pere-Lluís Huguet Cabot, Edoardo Barba, Roberto Navigli
A test suite of prompt injection attacks for LLM-based machine translation
Antonio Valerio Miceli-Barone, Zhifan Sun
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