566 citations · 1.6k across the 28 of their papers we have counts for
28 papers
Quantum transport in a one-dimensional quasicrystal with mobility edges
Yan Xing, Lu Qi, Xuedong Zhao +4
Quantum transport in a one-dimensional (1D) quasiperiodic lattice with mobility edges is explored. We first investigate the adiabatic pumping between left and right edge modes by r…
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Shuai Lu, Daya Guo, Shuo Ren +19
Benchmark datasets have a significant impact on accelerating research in programming language tasks. In this paper, we introduce CodeXGLUE, a benchmark dataset to foster machine le…
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
Chengyi Wang, Yu Wu, Yao Qian +5
In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC le…
Microsoft Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2020
Xiong Xiao, Naoyuki Kanda, Zhuo Chen +10
This paper describes the Microsoft speaker diarization system for monaural multi-talker recordings in the wild, evaluated at the diarization track of the VoxCeleb Speaker Recogniti…
CodeBLEU: a Method for Automatic Evaluation of Code Synthesis
Shuo Ren, Daya Guo, Shuai Lu +7
Evaluation metrics play a vital role in the growth of an area as it defines the standard of distinguishing between good and bad models. In the area of code synthesis, the commonly…
GraphCodeBERT: Pre-training Code Representations with Data Flow
Daya Guo, Shuo Ren, Shuai Lu +15
Pre-trained models for programming language have achieved dramatic empirical improvements on a variety of code-related tasks such as code search, code completion, code summarizatio…
MoBoAligner: a Neural Alignment Model for Non-autoregressive TTS with Monotonic Boundary Search
Naihan Li, Shujie Liu, Yanqing Liu +3
To speed up the inference of neural speech synthesis, non-autoregressive models receive increasing attention recently. In non-autoregressive models, additional durations of text to…
On the Comparison of Popular End-to-End Models for Large Scale Speech Recognition
Jinyu Li, Yu Wu, Yashesh Gaur +3
Recently, there has been a strong push to transition from hybrid models to end-to-end (E2E) models for automatic speech recognition. Currently, there are three promising E2E method…
Curriculum Pre-training for End-to-End Speech Translation
Chengyi Wang, Yu Wu, Shujie Liu +2
End-to-end speech translation poses a heavy burden on the encoder, because it has to transcribe, understand, and learn cross-lingual semantics simultaneously. To obtain a powerful…
Low Latency End-to-End Streaming Speech Recognition with a Scout Network
Chengyi Wang, Yu Wu, Shujie Liu +4
The attention-based Transformer model has achieved promising results for speech recognition (SR) in the offline mode. However, in the streaming mode, the Transformer model usually…
Semantic Mask for Transformer based End-to-End Speech Recognition
Chengyi Wang, Yu Wu, Yujiao Du +7
Attention-based encoder-decoder model has achieved impressive results for both automatic speech recognition (ASR) and text-to-speech (TTS) tasks. This approach takes advantage of t…
Unsupervised Context Rewriting for Open Domain Conversation
Kun Zhou, Kai Zhang, Yu Wu +2
Context modeling has a pivotal role in open domain conversation. Existing works either use heuristic methods or jointly learn context modeling and response generation with an encod…
Accelerating Transformer Decoding via a Hybrid of Self-attention and Recurrent Neural Network
Chengyi Wang, Shuangzhi Wu, Shujie Liu
Due to the highly parallelizable architecture, Transformer is faster to train than RNN-based models and popularly used in machine translation tasks. However, at inference time, eac…
Source Dependency-Aware Transformer with Supervised Self-Attention
Chengyi Wang, Shuangzhi Wu, Shujie Liu
Recently, Transformer has achieved the state-of-the-art performance on many machine translation tasks. However, without syntax knowledge explicitly considered in the encoder, incor…
Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation
Chengyi Wang, Yu Wu, Shujie Liu +2
End-to-end speech translation, a hot topic in recent years, aims to translate a segment of audio into a specific language with an end-to-end model. Conventional approaches employ m…
Explicit Cross-lingual Pre-training for Unsupervised Machine Translation
Shuo Ren, Yu Wu, Shujie Liu +2
Pre-training has proven to be effective in unsupervised machine translation due to its ability to model deep context information in cross-lingual scenarios. However, the cross-ling…
Unsupervised Neural Machine Translation with SMT as Posterior Regularization
Shuo Ren, Zhirui Zhang, Shujie Liu +2
Without real bilingual corpus available, unsupervised Neural Machine Translation (NMT) typically requires pseudo parallel data generated with the back-translation method for the mo…
Approximate Distribution Matching for Sequence-to-Sequence Learning
Wenhu Chen, Guanlin Li, Shujie Liu +3
Sequence-to-Sequence models were introduced to tackle many real-life problems like machine translation, summarization, image captioning, etc. The standard optimization algorithms a…
Neural Speech Synthesis with Transformer Network
Naihan Li, Shujie Liu, Yanqing Liu +3
Although end-to-end neural text-to-speech (TTS) methods (such as Tacotron2) are proposed and achieve state-of-the-art performance, they still suffer from two problems: 1) low effic…
Regularizing Neural Machine Translation by Target-bidirectional Agreement
Zhirui Zhang, Shuangzhi Wu, Shujie Liu +3
Although Neural Machine Translation (NMT) has achieved remarkable progress in the past several years, most NMT systems still suffer from a fundamental shortcoming as in other seque…
Triangular Architecture for Rare Language Translation
Shuo Ren, Wenhu Chen, Shujie Liu +3
Neural Machine Translation (NMT) performs poor on the low-resource language pair , especially when is a rare language. By introducing another rich language , we propo…
Achieving Human Parity on Automatic Chinese to English News Translation
Hany Hassan, Anthony Aue, Chang Chen +21
Machine translation has made rapid advances in recent years. Millions of people are using it today in online translation systems and mobile applications in order to communicate acr…
Joint Training for Neural Machine Translation Models with Monolingual Data
Zhirui Zhang, Shujie Liu, Mu Li +2
Monolingual data have been demonstrated to be helpful in improving translation quality of both statistical machine translation (SMT) systems and neural machine translation (NMT) sy…
Assertion-based QA with Question-Aware Open Information Extraction
Zhao Yan, Duyu Tang, Nan Duan +5
We present assertion based question answering (ABQA), an open domain question answering task that takes a question and a passage as inputs, and outputs a semi-structured assertion…