480 citations · 1.4k across the 8 of their papers we have counts for
8 papers
Interactive Editing for Text Summarization
Yujia Xie, Xun Wang, Si-Qing Chen +2
Summarizing lengthy documents is a common and essential task in our daily lives. Although recent advancements in neural summarization models can assist in crafting general-purpose…
Momentum Calibration for Text Generation
Xingxing Zhang, Yiran Liu, Xun Wang +5
The input and output of most text generation tasks can be transformed to two sequences of tokens and they can be modeled using sequence-to-sequence learning modeling tools such as…
Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization
Pengcheng He, Baolin Peng, Liyang Lu +11
This paper presents Z-Code++, a new pre-trained language model optimized for abstractive text summarization. The model extends the state of the art encoder-decoder model using thre…
Advances in Online Audio-Visual Meeting Transcription
Takuya Yoshioka, Igor Abramovski, Cem Aksoylar +23
This paper describes a system that generates speaker-annotated transcripts of meetings by using a microphone array and a 360-degree camera. The hallmark of the system is its abilit…
The Microsoft 2017 Conversational Speech Recognition System
W. Xiong, L. Wu, F. Alleva +3
We describe the 2017 version of Microsoft's conversational speech recognition system, in which we update our 2016 system with recent developments in neural-network-based acoustic a…
Progressive Joint Modeling in Unsupervised Single-channel Overlapped Speech Recognition
Zhehuai Chen, Jasha Droppo, Jinyu Li +1
Unsupervised single-channel overlapped speech recognition is one of the hardest problems in automatic speech recognition (ASR). Permutation invariant training (PIT) is a state of t…
Achieving Human Parity in Conversational Speech Recognition
W. Xiong, J. Droppo, X. Huang +5
Conversational speech recognition has served as a flagship speech recognition task since the release of the Switchboard corpus in the 1990s. In this paper, we measure the human err…
The Microsoft 2016 Conversational Speech Recognition System
W. Xiong, J. Droppo, X. Huang +5
We describe Microsoft's conversational speech recognition system, in which we combine recent developments in neural-network-based acoustic and language modeling to advance the stat…