Anxiang Ma


2022

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The NiuTrans’s Submission to the IWSLT22 English-to-Chinese Offline Speech Translation Task
Yuhao Zhang | Canan Huang | Chen Xu | Xiaoqian Liu | Bei Li | Anxiang Ma | Tong Xiao | Jingbo Zhu
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

This paper describes NiuTrans’s submission to the IWSLT22 English-to-Chinese (En-Zh) offline speech translation task. The end-to-end and bilingual system is built by constrained English and Chinese data and translates the English speech to Chinese text without intermediate transcription. Our speech translation models are composed of different pre-trained acoustic models and machine translation models by two kinds of adapters. We compared the effect of the standard speech feature (e.g. log Mel-filterbank) and the pre-training speech feature and try to make them interact. The final submission is an ensemble of three potential speech translation models. Our single best and ensemble model achieves 18.66 BLEU and 19.35 BLEU separately on MuST-C En-Zh tst-COMMON set.

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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.