Chuanhao Lv


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Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering Pairs
Yuxin Zuo | Bei Li | Chuanhao Lv | Tong Zheng | Tong Xiao | JingBo Zhu
Findings of the Association for Computational Linguistics: EMNLP 2023

This paper presents an in-depth study of multimodal machine translation (MMT), examining the prevailing understanding that MMT systems exhibit decreased sensitivity to visual information when text inputs are complete. Instead, we attribute this phenomenon to insufficient cross-modal interaction, rather than image information redundancy. A novel approach is proposed to generate parallel Visual Question-Answering (VQA) style pairs from the source text, fostering more robust cross-modal interaction. Using Large Language Models (LLMs), we explicitly model the probing signal in MMT to convert it into VQA-style data to create the Multi30K-VQA dataset. An MMT-VQA multitask learning framework is introduced to incorporate explicit probing signals from the dataset into the MMT training process. Experimental results on two widely-used benchmarks demonstrate the effectiveness of this novel approach. Our code and data would be available at:


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On Vision Features in Multimodal Machine Translation
Bei Li | Chuanhao Lv | Zefan Zhou | Tao Zhou | Tong Xiao | Anxiang Ma | JingBo Zhu
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Previous work on multimodal machine translation (MMT) has focused on the way of incorporating vision features into translation but little attention is on the quality of vision models. In this work, we investigate the impact of vision models on MMT. Given the fact that Transformer is becoming popular in computer vision, we experiment with various strong models (such as Vision Transformer) and enhanced features (such as object-detection and image captioning). We develop a selective attention model to study the patch-level contribution of an image in MMT. On detailed probing tasks, we find that stronger vision models are helpful for learning translation from the visual modality. Our results also suggest the need of carefully examining MMT models, especially when current benchmarks are small-scale and biased.


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The NiuTrans Machine Translation Systems for WMT21
Shuhan Zhou | Tao Zhou | Binghao Wei | Yingfeng Luo | Yongyu Mu | Zefan Zhou | Chenglong Wang | Xuanjun Zhou | Chuanhao Lv | Yi Jing | Laohu Wang | Jingnan Zhang | Canan Huang | Zhongxiang Yan | Chi Hu | Bei Li | Tong Xiao | Jingbo Zhu
Proceedings of the Sixth Conference on Machine Translation

This paper describes NiuTrans neural machine translation systems of the WMT 2021 news translation tasks. We made submissions to 9 language directions, including English2Chinese, Japanese, Russian, Icelandic and English2Hausa tasks. Our primary systems are built on several effective variants of Transformer, e.g., Transformer-DLCL, ODE-Transformer. We also utilize back-translation, knowledge distillation, post-ensemble, and iterative fine-tuning techniques to enhance the model performance further.