45 citations · 301 across the 53 of their papers we have counts for
15 papers · 1 filter
Reciprocal Supervised Learning Improves Neural Machine Translation
Minkai Xu, Mingxuan Wang, Zhouhan Lin +3
Despite the recent success on image classification, self-training has only achieved limited gains on structured prediction tasks such as neural machine translation (NMT). This is m…
Finding Sparse Structures for Domain Specific Neural Machine Translation
Jianze Liang, Chengqi Zhao, Mingxuan Wang +2
Neural machine translation often adopts the fine-tuning approach to adapt to specific domains. However, nonrestricted fine-tuning can easily degrade on the general domain and over-…
NeurST: Neural Speech Translation Toolkit
Chengqi Zhao, Mingxuan Wang, Qianqian Dong +2
NeurST is an open-source toolkit for neural speech translation. The toolkit mainly focuses on end-to-end speech translation, which is easy to use, modify, and extend to advanced sp…
The Volctrans Machine Translation System for WMT20
Liwei Wu, Xiao Pan, Zehui Lin +3
This paper describes our VolcTrans system on WMT20 shared news translation task. We participated in 8 translation directions. Our basic systems are based on Transformer, with sever…
On the Sentence Embeddings from Pre-trained Language Models
Bohan Li, Hao Zhou, Junxian He +3
Pre-trained contextual representations like BERT have achieved great success in natural language processing. However, the sentence embeddings from the pre-trained language models w…
Volctrans Parallel Corpus Filtering System for WMT 2020
Runxin Xu, Zhuo Zhi, Jun Cao +2
In this paper, we describe our submissions to the WMT20 shared task on parallel corpus filtering and alignment for low-resource conditions. The task requires the participants to al…
LightSeq: A High Performance Inference Library for Transformers
Xiaohui Wang, Ying Xiong, Yang Wei +2
Transformer, BERT and their variants have achieved great success in natural language processing. Since Transformer models are huge in size, serving these models is a challenge for…
Rethinking Document-level Neural Machine Translation
Zewei Sun, Mingxuan Wang, Hao Zhou +4
This paper does not aim at introducing a novel model for document-level neural machine translation. Instead, we head back to the original Transformer model and hope to answer the f…
Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information
Zehui Lin, Xiao Pan, Mingxuan Wang +4
We investigate the following question for machine translation (MT): can we develop a single universal MT model to serve as the common seed and obtain derivative and improved models…
Consecutive Decoding for Speech-to-text Translation
Qianqian Dong, Mingxuan Wang, Hao Zhou +3
Speech-to-text translation (ST), which directly translates the source language speech to the target language text, has attracted intensive attention recently. However, the combinat…
"Listen, Understand and Translate": Triple Supervision Decouples End-to-end Speech-to-text Translation
Qianqian Dong, Rong Ye, Mingxuan Wang +4
An end-to-end speech-to-text translation (ST) takes audio in a source language and outputs the text in a target language. Existing methods are limited by the amount of parallel cor…
Generative Imagination Elevates Machine Translation
Quanyu Long, Mingxuan Wang, Lei Li
There are common semantics shared across text and images. Given a sentence in a source language, whether depicting the visual scene helps translation into a target language? Existi…
Glancing Transformer for Non-Autoregressive Neural Machine Translation
Lihua Qian, Hao Zhou, Yu Bao +5
Recent work on non-autoregressive neural machine translation (NAT) aims at improving the efficiency by parallel decoding without sacrificing the quality. However, existing NAT meth…
Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation
Yuxuan Song, Ning Miao, Hao Zhou +3
Auto-regressive sequence generative models trained by Maximum Likelihood Estimation suffer the exposure bias problem in practical finite sample scenarios. The crux is that the numb…
Cross-lingual Supervision Improves Unsupervised Neural Machine Translation
Mingxuan Wang, Hongxiao Bai, Hai Zhao +1
Neural machine translation~(NMT) is ineffective for zero-resource languages. Recent works exploring the possibility of unsupervised neural machine translation (UNMT) with only mono…