Search Results for author: Mingxuan Wang

Found 47 papers, 20 papers with code

The Volctrans GLAT System: Non-autoregressive Translation Meets WMT21

no code implementations23 Sep 2021 Lihua Qian, Yi Zhou, Zaixiang Zheng, Yaoming Zhu, Zehui Lin, Jiangtao Feng, Shanbo Cheng, Lei LI, Mingxuan Wang, Hao Zhou

This paper describes the Volctrans' submission to the WMT21 news translation shared task for German->English translation.

Learning Kernel-Smoothed Machine Translation with Retrieved Examples

1 code implementation21 Sep 2021 Qingnan Jiang, Mingxuan Wang, Jun Cao, Shanbo Cheng, ShuJian Huang, Lei LI

How to effectively adapt neural machine translation (NMT) models according to emerging cases without retraining?

Domain Adaptation Machine Translation

UniST: Unified End-to-end Model for Streaming and Non-streaming Speech Translation

no code implementations15 Sep 2021 Qianqian Dong, Yaoming Zhu, Mingxuan Wang, Lei LI

This paper presents a unified end-to-end frame-work for both streaming and non-streamingspeech translation.

Speech-to-Text Translation

Multilingual Translation via Grafting Pre-trained Language Models

1 code implementation11 Sep 2021 Zewei Sun, Mingxuan Wang, Lei LI

Can pre-trained BERT for one language and GPT for another be glued together to translate texts?

Machine Translation

Secoco: Self-Correcting Encoding for Neural Machine Translation

no code implementations27 Aug 2021 Tao Wang, Chengqi Zhao, Mingxuan Wang, Lei LI, Hang Li, Deyi Xiong

This paper presents Self-correcting Encoding (Secoco), a framework that effectively deals with input noise for robust neural machine translation by introducing self-correcting predictors.

Machine Translation

Pre-training Methods for Neural Machine Translation

no code implementations ACL 2021 Mingxuan Wang, Lei LI

This tutorial provides a comprehensive guide to make the most of pre-training for neural machine translation.

Machine Translation

Language Tags Matter for Zero-Shot Neural Machine Translation

no code implementations15 Jun 2021 Liwei Wu, Shanbo Cheng, Mingxuan Wang, Lei LI

Language tag (LT) strategies are often adopted to indicate the translation directions in MNMT.

Machine Translation

Contrastive Learning for Many-to-many Multilingual Neural Machine Translation

3 code implementations ACL 2021 Xiao Pan, Mingxuan Wang, Liwei Wu, Lei LI

Existing multilingual machine translation approaches mainly focus on English-centric directions, while the non-English directions still lag behind.

Contrastive Learning Data Augmentation +1

Learning Language Specific Sub-network for Multilingual Machine Translation

1 code implementation ACL 2021 Zehui Lin, Liwei Wu, Mingxuan Wang, Lei LI

These jointly trained models often suffer from performance degradation on rich-resource language pairs.

Machine Translation

The Volctrans Neural Speech Translation System for IWSLT 2021

1 code implementation16 May 2021 Chengqi Zhao, Zhicheng Liu, Jian Tong, Tao Wang, Mingxuan Wang, Rong Ye, Qianqian Dong, Jun Cao, Lei LI

For offline speech translation, our best end-to-end model achieves 8. 1 BLEU improvements over the benchmark on the MuST-C test set and is even approaching the results of a strong cascade solution.

Learning Shared Semantic Space for Speech-to-Text Translation

2 code implementations7 May 2021 Chi Han, Mingxuan Wang, Heng Ji, Lei LI

By projecting audio and text features to a common semantic representation, Chimera unifies MT and ST tasks and boosts the performance on ST benchmarks, MuST-C and Augmented Librispeech, to a new state-of-the-art.

Machine Translation Speech-to-Text Translation

End-to-end Speech Translation via Cross-modal Progressive Training

1 code implementation21 Apr 2021 Rong Ye, Mingxuan Wang, Lei LI

XSTNet takes both speech and text as input and outputs both transcription and translation text.

Machine Translation Speech-to-Text Translation

Non-iterative Parallel Text Generation via Glancing Transformer

no code implementations1 Jan 2021 Lihua Qian, Hao Zhou, Yu Bao, Mingxuan Wang, Lin Qiu, Weinan Zhang, Yong Yu, Lei LI

Although non-autoregressive models with one-iteration generation achieves remarkable inference speed-up, they still falls behind their autoregressive counterparts inprediction accuracy.

Language Modelling Text Generation

Reciprocal Supervised Learning Improves Neural Machine Translation

1 code implementation5 Dec 2020 Minkai Xu, Mingxuan Wang, Zhouhan Lin, Hao Zhou, Weinan Zhang, Lei LI

Despite the recent success on image classification, self-training has only achieved limited gains on structured prediction tasks such as neural machine translation (NMT).

Image Classification Knowledge Distillation +2

Volctrans Parallel Corpus Filtering System for WMT 2020

no code implementations27 Oct 2020 Runxin Xu, Zhuo Zhi, Jun Cao, Mingxuan Wang, Lei LI

In this paper, we describe our submissions to the WMT20 shared task on parallel corpus filtering and alignment for low-resource conditions.

Word Alignment

Capturing Longer Context for Document-level Neural Machine Translation: A Multi-resolutional Approach

1 code implementation18 Oct 2020 Zewei Sun, Mingxuan Wang, Hao Zhou, Chengqi Zhao, ShuJian Huang, Jiajun Chen, Lei LI

It is quite a challenge to incorporate long document context in the prevailing neural machine translation models such as Transformer.

Document-level Machine Translation

Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information

1 code implementation EMNLP 2020 Zehui Lin, Xiao Pan, Mingxuan Wang, Xipeng Qiu, Jiangtao Feng, Hao Zhou, Lei LI

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 on arbitrary language pairs?

Ranked #3 on Machine Translation on WMT2014 English-French (using extra training data)

Machine Translation

Consecutive Decoding for Speech-to-text Translation

1 code implementation21 Sep 2020 Qianqian Dong, Mingxuan Wang, Hao Zhou, Shuang Xu, Bo Xu, Lei LI

The key idea is to generate source transcript and target translation text with a single decoder.

Machine Translation Speech Recognition +1

Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation

no code implementations12 Jul 2020 Yuxuan Song, Ning Miao, Hao Zhou, Lantao Yu, Mingxuan Wang, Lei LI

Auto-regressive sequence generative models trained by Maximum Likelihood Estimation suffer the exposure bias problem in practical finite sample scenarios.

Density Ratio Estimation Text Generation

Xiaomingbot: A Multilingual Robot News Reporter

no code implementations ACL 2020 Runxin Xu, Jun Cao, Mingxuan Wang, Jiaze Chen, Hao Zhou, Ying Zeng, Yu-Ping Wang, Li Chen, Xiang Yin, Xijin Zhang, Songcheng Jiang, Yuxuan Wang, Lei LI

This paper proposes the building of Xiaomingbot, an intelligent, multilingual and multimodal software robot equipped with four integral capabilities: news generation, news translation, news reading and avatar animation.

News Generation

Towards Making the Most of BERT in Neural Machine Translation

1 code implementation15 Aug 2019 Jiacheng Yang, Mingxuan Wang, Hao Zhou, Chengqi Zhao, Yong Yu, Wei-Nan Zhang, Lei LI

GPT-2 and BERT demonstrate the effectiveness of using pre-trained language models (LMs) on various natural language processing tasks.

Machine Translation

Towards Linear Time Neural Machine Translation with Capsule Networks

no code implementations IJCNLP 2019 Mingxuan Wang, Jun Xie, Zhixing Tan, Jinsong Su, Deyi Xiong, Lei LI

In this study, we first investigate a novel capsule network with dynamic routing for linear time Neural Machine Translation (NMT), referred as \textsc{CapsNMT}.

Machine Translation

Deep Semantic Role Labeling with Self-Attention

1 code implementation5 Dec 2017 Zhixing Tan, Mingxuan Wang, Jun Xie, Yidong Chen, Xiaodong Shi

Semantic Role Labeling (SRL) is believed to be a crucial step towards natural language understanding and has been widely studied.

Natural Language Understanding Semantic Role Labeling

Incorporating Word Reordering Knowledge into Attention-based Neural Machine Translation

no code implementations ACL 2017 Jinchao Zhang, Mingxuan Wang, Qun Liu, Jie zhou

This paper proposes three distortion models to explicitly incorporate the word reordering knowledge into attention-based Neural Machine Translation (NMT) for further improving translation performance.

Machine Translation Word Alignment

Deep Neural Machine Translation with Linear Associative Unit

no code implementations ACL 2017 Mingxuan Wang, Zhengdong Lu, Jie zhou, Qun Liu

Deep Neural Networks (DNNs) have provably enhanced the state-of-the-art Neural Machine Translation (NMT) with their capability in modeling complex functions and capturing complex linguistic structures.

Machine Translation

Memory-enhanced Decoder for Neural Machine Translation

no code implementations EMNLP 2016 Mingxuan Wang, Zhengdong Lu, Hang Li, Qun Liu

We propose to enhance the RNN decoder in a neural machine translator (NMT) with external memory, as a natural but powerful extension to the state in the decoding RNN.

Machine Translation

$gen$CNN: A Convolutional Architecture for Word Sequence Prediction

no code implementations17 Mar 2015 Mingxuan Wang, Zhengdong Lu, Hang Li, Wenbin Jiang, Qun Liu

Different from previous work on neural network-based language modeling and generation (e. g., RNN or LSTM), we choose not to greedily summarize the history of words as a fixed length vector.

Language Modelling Machine Translation +2

Syntax-based Deep Matching of Short Texts

no code implementations9 Mar 2015 Mingxuan Wang, Zhengdong Lu, Hang Li, Qun Liu

Many tasks in natural language processing, ranging from machine translation to question answering, can be reduced to the problem of matching two sentences or more generally two short texts.

Machine Translation Question Answering

Encoding Source Language with Convolutional Neural Network for Machine Translation

no code implementations IJCNLP 2015 Fandong Meng, Zhengdong Lu, Mingxuan Wang, Hang Li, Wenbin Jiang, Qun Liu

The recently proposed neural network joint model (NNJM) (Devlin et al., 2014) augments the n-gram target language model with a heuristically chosen source context window, achieving state-of-the-art performance in SMT.

Language Modelling Machine Translation

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