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26 papers · 1 filter
A Study of Multilingual Neural Machine Translation
Xu Tan, Yichong Leng, Jiale Chen +3
Multilingual neural machine translation (NMT) has recently been investigated from different aspects (e.g., pivot translation, zero-shot translation, fine-tuning, or training from s…
Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation
Junliang Guo, Xu Tan, Linli Xu +3
Non-autoregressive translation (NAT) models remove the dependence on previous target tokens and generate all target tokens in parallel, resulting in significant inference speedup b…
Microsoft Research Asia's Systems for WMT19
Yingce Xia, Xu Tan, Fei Tian +11
We Microsoft Research Asia made submissions to 11 language directions in the WMT19 news translation tasks. We won the first place for 8 of the 11 directions and the second place fo…
Distributional Reward Decomposition for Reinforcement Learning
Zichuan Lin, Li Zhao, Derek Yang +3
Many reinforcement learning (RL) tasks have specific properties that can be leveraged to modify existing RL algorithms to adapt to those tasks and further improve performance, and…
Fully Parameterized Quantile Function for Distributional Reinforcement Learning
Derek Yang, Li Zhao, Zichuan Lin +3
Distributional Reinforcement Learning (RL) differs from traditional RL in that, rather than the expectation of total returns, it estimates distributions and has achieved state-of-t…
Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base
Tao Shen, Xiubo Geng, Tao Qin +5
We consider the problem of conversational question answering over a large-scale knowledge base. To handle huge entity vocabulary of a large-scale knowledge base, recent neural sema…
Hint-Based Training for Non-Autoregressive Machine Translation
Zhuohan Li, Zi Lin, Di He +4
Due to the unparallelizable nature of the autoregressive factorization, AutoRegressive Translation (ART) models have to generate tokens sequentially during decoding and thus suffer…
Balanced One-shot Neural Architecture Optimization
Renqian Luo, Tao Qin, Enhong Chen
The ability to rank candidate architectures is the key to the performance of neural architecture search~(NAS). One-shot NAS is proposed to reduce the expense but shows inferior per…
Efficient Bidirectional Neural Machine Translation
Xu Tan, Yingce Xia, Lijun Wu +1
The encoder-decoder based neural machine translation usually generates a target sequence token by token from left to right. Due to error propagation, the tokens in the right side o…
Multilingual Neural Machine Translation with Language Clustering
Xu Tan, Jiale Chen, Di He +3
Multilingual neural machine translation (NMT), which translates multiple languages using a single model, is of great practical importance due to its advantages in simplifying the t…
Hard but Robust, Easy but Sensitive: How Encoder and Decoder Perform in Neural Machine Translation
Tianyu He, Xu Tan, Tao Qin
Neural machine translation (NMT) typically adopts the encoder-decoder framework. A good understanding of the characteristics and functionalities of the encoder and decoder can help…
Language Graph Distillation for Low-Resource Machine Translation
Tianyu He, Jiale Chen, Xu Tan +1
Neural machine translation on low-resource language is challenging due to the lack of bilingual sentence pairs. Previous works usually solve the low-resource translation problem wi…
Representation Degeneration Problem in Training Natural Language Generation Models
Jun Gao, Di He, Xu Tan +3
We study an interesting problem in training neural network-based models for natural language generation tasks, which we call the \emph{representation degeneration problem}. We obse…
Depth Growing for Neural Machine Translation
Lijun Wu, Yiren Wang, Yingce Xia +5
While very deep neural networks have shown effectiveness for computer vision and text classification applications, how to increase the network depth of neural machine translation (…
Unsupervised Pivot Translation for Distant Languages
Yichong Leng, Xu Tan, Tao Qin +2
Unsupervised neural machine translation (NMT) has attracted a lot of attention recently. While state-of-the-art methods for unsupervised translation usually perform well between si…
MASS: Masked Sequence to Sequence Pre-training for Language Generation
Kaitao Song, Xu Tan, Tao Qin +2
Pre-training and fine-tuning, e.g., BERT, have achieved great success in language understanding by transferring knowledge from rich-resource pre-training task to the low/zero-resou…
Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View
Yiping Lu, Zhuohan Li, Di He +5
The Transformer architecture is widely used in natural language processing. Despite its success, the design principle of the Transformer remains elusive. In this paper, we provide…
What and Where to Translate: Local Mask-based Image-to-Image Translation
Wonwoong Cho, Seunghwan Choi, Junwoo Park +3
Recently, image-to-image translation has obtained significant attention. Among many, those approaches based on an exemplar image that contains the target style information has been…
Image-to-Image Translation with Multi-Path Consistency Regularization
Jianxin Lin, Yingce Xia, Yijun Wang +2
Image translation across different domains has attracted much attention in both machine learning and computer vision communities. Taking the translation from source domain $\mathca…
Soft Contextual Data Augmentation for Neural Machine Translation
Jinhua Zhu, Fei Gao, Lijun Wu +5
While data augmentation is an important trick to boost the accuracy of deep learning methods in computer vision tasks, its study in natural language tasks is still very limited. In…
Almost Unsupervised Text to Speech and Automatic Speech Recognition
Yi Ren, Xu Tan, Tao Qin +3
Text to speech (TTS) and automatic speech recognition (ASR) are two dual tasks in speech processing and both achieve impressive performance thanks to the recent advance in deep lea…
Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion
Hao Sun, Xu Tan, Jun-Wei Gan +4
Grapheme-to-phoneme (G2P) conversion is an important task in automatic speech recognition and text-to-speech systems. Recently, G2P conversion is viewed as a sequence to sequence t…
Competitive Bridge Bidding with Deep Neural Networks
Jiang Rong, Tao Qin, Bo An
The game of bridge consists of two stages: bidding and playing. While playing is proved to be relatively easy for computer programs, bidding is very challenging. During the bidding…
Non-Autoregressive Machine Translation with Auxiliary Regularization
Yiren Wang, Fei Tian, Di He +3
As a new neural machine translation approach, Non-Autoregressive machine Translation (NAT) has attracted attention recently due to its high efficiency in inference. However, the hi…