Chuanqiang Zhang


pdf bib
Dynamic Sentence Boundary Detection for Simultaneous Translation
Ruiqing Zhang | Chuanqiang Zhang
Proceedings of the First Workshop on Automatic Simultaneous Translation

Simultaneous Translation is a great challenge in which translation starts before the source sentence finished. Most studies take transcription as input and focus on balancing translation quality and latency for each sentence. However, most ASR systems can not provide accurate sentence boundaries in realtime. Thus it is a key problem to segment sentences for the word streaming before translation. In this paper, we propose a novel method for sentence boundary detection that takes it as a multi-class classification task under the end-to-end pre-training framework. Experiments show significant improvements both in terms of translation quality and latency.

pdf bib
Learning Adaptive Segmentation Policy for Simultaneous Translation
Ruiqing Zhang | Chuanqiang Zhang | Zhongjun He | Hua Wu | Haifeng Wang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Balancing accuracy and latency is a great challenge for simultaneous translation. To achieve high accuracy, the model usually needs to wait for more streaming text before translation, which results in increased latency. However, keeping low latency would probably hurt accuracy. Therefore, it is essential to segment the ASR output into appropriate units for translation. Inspired by human interpreters, we propose a novel adaptive segmentation policy for simultaneous translation. The policy learns to segment the source text by considering possible translations produced by the translation model, maintaining consistency between the segmentation and translation. Experimental results on Chinese-English and German-English translation show that our method achieves a better accuracy-latency trade-off over recently proposed state-of-the-art methods.


pdf bib
STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework
Mingbo Ma | Liang Huang | Hao Xiong | Renjie Zheng | Kaibo Liu | Baigong Zheng | Chuanqiang Zhang | Zhongjun He | Hairong Liu | Xing Li | Hua Wu | Haifeng Wang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Simultaneous translation, which translates sentences before they are finished, is use- ful in many scenarios but is notoriously dif- ficult due to word-order differences. While the conventional seq-to-seq framework is only suitable for full-sentence translation, we pro- pose a novel prefix-to-prefix framework for si- multaneous translation that implicitly learns to anticipate in a single translation model. Within this framework, we present a very sim- ple yet surprisingly effective “wait-k” policy trained to generate the target sentence concur- rently with the source sentence, but always k words behind. Experiments show our strat- egy achieves low latency and reasonable qual- ity (compared to full-sentence translation) on 4 directions: zh↔en and de↔en.