1 citations · 2 across the 11 of their papers we have counts for
11 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…
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…
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…
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…
Improving Language Transfer Capability of Decoder-only Architecture in Multilingual Neural Machine Translation
Zhi Qu, Yiran Wang, Chenchen Ding +3
Existing multilingual neural machine translation (MNMT) approaches mainly focus on improving models with the encoder-decoder architecture to translate multiple languages. However,…
Exploring Intrinsic Language-specific Subspaces in Fine-tuning Multilingual Neural Machine Translation
Zhe Cao, Zhi Qu, Hidetaka Kamigaito +1
Multilingual neural machine translation models support fine-tuning hundreds of languages simultaneously. However, fine-tuning on full parameters solely is inefficient potentially l…
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…
Sharing Parameter by Conjugation for Knowledge Graph Embeddings in Complex Space
Xincan Feng, Zhi Qu, Yuchang Cheng +2
A Knowledge Graph (KG) is the directed graphical representation of entities and relations in the real world. KG can be applied in diverse Natural Language Processing (NLP) tasks wh…
Cross-lingual Contextualized Phrase Retrieval
Huayang Li, Deng Cai, Zhi Qu +4
Phrase-level dense retrieval has shown many appealing characteristics in downstream NLP tasks by leveraging the fine-grained information that phrases offer. In our work, we propose…
Adapting to Non-Centered Languages for Zero-shot Multilingual Translation
Zhi Qu, Taro Watanabe
Multilingual neural machine translation can translate unseen language pairs during training, i.e. zero-shot translation. However, the zero-shot translation is always unstable. Alth…