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Fusing Context Into Knowledge Graph for Commonsense Question Answering
Yichong Xu, Chenguang Zhu, Ruochen Xu +3
Commonsense question answering (QA) requires a model to grasp commonsense and factual knowledge to answer questions about world events. Many prior methods couple language modeling…
LSTM-LM with Long-Term History for First-Pass Decoding in Conversational Speech Recognition
Xie Chen, Sarangarajan Parthasarathy, William Gale +2
LSTM language models (LSTM-LMs) have been proven to be powerful and yielded significant performance improvements over count based n-gram LMs in modern speech recognition systems. D…
Mixed-Lingual Pre-training for Cross-lingual Summarization
Ruochen Xu, Chenguang Zhu, Yu Shi +2
Cross-lingual Summarization (CLS) aims at producing a summary in the target language for an article in the source language. Traditional solutions employ a two-step approach, i.e. t…
JAKET: Joint Pre-training of Knowledge Graph and Language Understanding
Donghan Yu, Chenguang Zhu, Yiming Yang +1
Knowledge graphs (KGs) contain rich information about world knowledge, entities and relations. Thus, they can be great supplements to existing pre-trained language models. However,…
SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding
Yu-An Chung, Chenguang Zhu, Michael Zeng
Spoken language understanding (SLU) requires a model to analyze input acoustic signal to understand its linguistic content and make predictions. To boost the models' performance, v…
Mind The Facts: Knowledge-Boosted Coherent Abstractive Text Summarization
Beliz Gunel, Chenguang Zhu, Michael Zeng +1
Neural models have become successful at producing abstractive summaries that are human-readable and fluent. However, these models have two critical shortcomings: they often don't r…
Meta Dialogue Policy Learning
Yumo Xu, Chenguang Zhu, Baolin Peng +1
Dialog policy determines the next-step actions for agents and hence is central to a dialogue system. However, when migrated to novel domains with little data, a policy model can fa…
Improving Readability for Automatic Speech Recognition Transcription
Junwei Liao, Sefik Emre Eskimez, Liyang Lu +5
Modern Automatic Speech Recognition (ASR) systems can achieve high performance in terms of recognition accuracy. However, a perfectly accurate transcript still can be challenging t…
Data Augmentation for Spoken Language Understanding via Pretrained Language Models
Baolin Peng, Chenguang Zhu, Michael Zeng +1
The training of spoken language understanding (SLU) models often faces the problem of data scarcity. In this paper, we put forward a data augmentation method using pretrained langu…
A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining
Chenguang Zhu, Ruochen Xu, Michael Zeng +1
With the abundance of automatic meeting transcripts, meeting summarization is of great interest to both participants and other parties. Traditional methods of summarizing meetings…
Enhancing Factual Consistency of Abstractive Summarization
Chenguang Zhu, William Hinthorn, Ruochen Xu +4
Automatic abstractive summaries are found to often distort or fabricate facts in the article. This inconsistency between summary and original text has seriously impacted its applic…
Few-shot Natural Language Generation for Task-Oriented Dialog
Baolin Peng, Chenguang Zhu, Chunyuan Li +4
As a crucial component in task-oriented dialog systems, the Natural Language Generation (NLG) module converts a dialog act represented in a semantic form into a response in natural…
TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising
Ziyi Yang, Chenguang Zhu, Robert Gmyr +3
Text summarization aims to extract essential information from a piece of text and transform the text into a concise version. Existing unsupervised abstractive summarization models…