Yaoming Zhu


2022

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Learning When to Translate for Streaming Speech
Qian Dong | Yaoming Zhu | Mingxuan Wang | Lei Li
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

How to find proper moments to generate partial sentence translation given a streaming speech input? Existing approaches waiting-and-translating for a fixed duration often break the acoustic units in speech, since the boundaries between acoustic units in speech are not even. In this paper, we propose MoSST, a simple yet effective method for translating streaming speech content. Given a usually long speech sequence, we develop an efficient monotonic segmentation module inside an encoder-decoder model to accumulate acoustic information incrementally and detect proper speech unit boundaries for the input in speech translation task. Experiments on multiple translation directions of the MuST-C dataset show that outperforms existing methods and achieves the best trade-off between translation quality (BLEU) and latency. Our code is available at https://github.com/dqqcasia/mosst.

2021

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The Volctrans GLAT System: Non-autoregressive Translation Meets WMT21
Lihua Qian | Yi Zhou | Zaixiang Zheng | Yaoming Zhu | Zehui Lin | Jiangtao Feng | Shanbo Cheng | Lei Li | Mingxuan Wang | Hao Zhou
Proceedings of the Sixth Conference on Machine Translation

This paper describes the Volctrans’ submission to the WMT21 news translation shared task for German->English translation. We build a parallel (i.e., non-autoregressive) translation system using the Glancing Transformer, which enables fast and accurate parallel decoding in contrast to the currently prevailing autoregressive models. To the best of our knowledge, this is the first parallel translation system that can be scaled to such a practical scenario like WMT competition. More importantly, our parallel translation system achieves the best BLEU score (35.0) on German->English translation task, outperforming all strong autoregressive counterparts.

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Counter-Interference Adapter for Multilingual Machine Translation
Yaoming Zhu | Jiangtao Feng | Chengqi Zhao | Mingxuan Wang | Lei Li
Findings of the Association for Computational Linguistics: EMNLP 2021

Developing a unified multilingual model has been a long pursuing goal for machine translation. However, existing approaches suffer from performance degradation - a single multilingual model is inferior to separately trained bilingual ones on rich-resource languages. We conjecture that such a phenomenon is due to interference brought by joint training with multiple languages. To accommodate the issue, we propose CIAT, an adapted Transformer model with a small parameter overhead for multilingual machine translation. We evaluate CIAT on multiple benchmark datasets, including IWSLT, OPUS-100, and WMT. Experiments show that the CIAT consistently outperforms strong multilingual baselines on 64 of total 66 language directions, 42 of which have above 0.5 BLEU improvement.

2020

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The Volctrans Machine Translation System for WMT20
Liwei Wu | Xiao Pan | Zehui Lin | Yaoming Zhu | Mingxuan Wang | Lei Li
Proceedings of the Fifth Conference on Machine Translation

This paper describes our submission systems for VolcTrans for WMT20 shared news translation task. We participated in 8 translation directions. Our basic systems are based on Transformer (CITATION), into which we also employed new architectures (bigger or deeper Transformers, dynamic convolution). The final systems include text pre-process, subword(a.k.a. BPE(CITATION)), baseline model training, iterative back-translation, model ensemble, knowledge distillation and multilingual pre-training.