566 citations · 1.6k across the 28 of their papers we have counts for
19 papers · 1 filter
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…
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…
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…
Implicit Distortion and Fertility Models for Attention-based Encoder-Decoder NMT Model
Shi Feng, Shujie Liu, Mu Li +1
Neural machine translation has shown very promising results lately. Most NMT models follow the encoder-decoder framework. To make encoder-decoder models more flexible, attention me…
Beyond Word-based Language Model in Statistical Machine Translation
Jiajun Zhang, Shujie Liu, Mu Li +2
Language model is one of the most important modules in statistical machine translation and currently the word-based language model dominants this community. However, many translati…
A Statistical Parsing Framework for Sentiment Classification
Li Dong, Furu Wei, Shujie Liu +2
We present a statistical parsing framework for sentence-level sentiment classification in this article. Unlike previous works that employ syntactic parsing results for sentiment an…