477 citations · 1.7k across the 84 of their papers we have counts for
13 papers · 2 filters
Minimum Bayes Risk Training of RNN-Transducer for End-to-End Speech Recognition
Chao Weng, Chengzhu Yu, Jia Cui +2
In this work, we propose minimum Bayes risk (MBR) training of RNN-Transducer (RNN-T) for end-to-end speech recognition. Specifically, initialized with a RNN-T trained model, MBR tr…
Joint Parsing and Generation for Abstractive Summarization
Kaiqiang Song, Logan Lebanoff, Qipeng Guo +5
Sentences produced by abstractive summarization systems can be ungrammatical and fail to preserve the original meanings, despite being locally fluent. In this paper we propose to r…
Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations
Sangwoo Cho, Chen Li, Dong Yu +2
Emerged as one of the best performing techniques for extractive summarization, determinantal point processes select the most probable set of sentences to form a summary according t…
Speech-XLNet: Unsupervised Acoustic Model Pretraining For Self-Attention Networks
Xingchen Song, Guangsen Wang, Zhiyong Wu +4
Self-attention network (SAN) can benefit significantly from the bi-directional representation learning through unsupervised pretraining paradigms such as BERT and XLNet. In this pa…
Improving Pre-Trained Multilingual Models with Vocabulary Expansion
Hai Wang, Dian Yu, Kai Sun +2
Recently, pre-trained language models have achieved remarkable success in a broad range of natural language processing tasks. However, in multilingual setting, it is extremely reso…
DurIAN: Duration Informed Attention Network For Multimodal Synthesis
Chengzhu Yu, Heng Lu, Na Hu +9
In this paper, we present a generic and robust multimodal synthesis system that produces highly natural speech and facial expression simultaneously. The key component of this syste…
Generating Diverse Story Continuations with Controllable Semantics
Lifu Tu, Xiaoan Ding, Dong Yu +1
We propose a simple and effective modeling framework for controlled generation of multiple, diverse outputs. We focus on the setting of generating the next sentence of a story give…
A Random Gossip BMUF Process for Neural Language Modeling
Yiheng Huang, Jinchuan Tian, Lei Han +4
Neural network language model (NNLM) is an essential component of industrial ASR systems. One important challenge of training an NNLM is to leverage between scaling the learning pr…
Knowledge-aware Pronoun Coreference Resolution
Hongming Zhang, Yan Song, Yangqiu Song +1
Resolving pronoun coreference requires knowledge support, especially for particular domains (e.g., medicine). In this paper, we explore how to leverage different types of knowledge…
Learning Word Embeddings with Domain Awareness
Guoyin Wang, Yan Song, Yue Zhang +1
Word embeddings are traditionally trained on a large corpus in an unsupervised setting, with no specific design for incorporating domain knowledge. This can lead to unsatisfactory…
Evidence Sentence Extraction for Machine Reading Comprehension
Hai Wang, Dian Yu, Kai Sun +4
Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks. However, it is still difficult to interpret the predictions of…
Improving Question Answering with External Knowledge
Xiaoman Pan, Kai Sun, Dian Yu +4
We focus on multiple-choice question answering (QA) tasks in subject areas such as science, where we require both broad background knowledge and the facts from the given subject-ar…
DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension
Kai Sun, Dian Yu, Jianshu Chen +3
We present DREAM, the first dialogue-based multiple-choice reading comprehension dataset. Collected from English-as-a-foreign-language examinations designed by human experts to eva…