8 papers
Rewrite to Translate, Translate to Reward: Reinforcement Learning for Source Rewriting in Machine Translation
Boxuan Lyu, Haiyue Song, Zhi Qu +3
Prior work has explored prompting large language models (LLMs) to rewrite source text before translation, with the goal of improving machine translation (MT) quality. However, we f…
CArtBench: Evaluating Vision-Language Models on Chinese Art Understanding, Interpretation, and Authenticity
Xuefeng Wei, Zhixuan Wang, Xuan Zhou +5
We introduce CARTBENCH, a museum-grounded benchmark for evaluating vision-language models (VLMs) on Chinese artworks beyond short-form recognition and QA. CARTBENCH comprises four…
Is Human Annotation Necessary? Iterative MBR Distillation for Error Span Detection in Machine Translation
Boxuan Lyu, Haiyue Song, Zhi Qu
Error Span Detection (ESD) is a crucial subtask in Machine Translation (MT) evaluation, aiming to identify the location and severity of translation errors. While fine-tuning models…
XQ-MEval: A Dataset with Cross-lingual Parallel Quality for Benchmarking Translation Metrics
Jingxuan Liu, Zhi Qu, Jin Tei +3
Automatic evaluation metrics are essential for building multilingual translation systems. The common practice of evaluating these systems is averaging metric scores across language…
Registering Source Tokens to Target Language Spaces in Multilingual Neural Machine Translation
Zhi Qu, Yiran Wang, Jiannan Mao +4
The multilingual neural machine translation (MNMT) aims for arbitrary translations across multiple languages. Although MNMT-specific models trained on parallel data offer low costs…
Languages Transferred Within the Encoder: On Representation Transfer in Zero-Shot Multilingual Translation
Zhi Qu, Chenchen Ding, Taro Watanabe
Understanding representation transfer in multilingual neural machine translation (MNMT) can reveal the reason for the zero-shot translation deficiency. In this work, we systematica…