Xuedong Huang

Also published as: X. Huang, X.D. Huang


2021

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Fusing Context Into Knowledge Graph for Commonsense Question Answering
Yichong Xu | Chenguang Zhu | Ruochen Xu | Yang Liu | Michael Zeng | Xuedong Huang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Enhancing Factual Consistency of Abstractive Summarization
Chenguang Zhu | William Hinthorn | Ruochen Xu | Qingkai Zeng | Michael Zeng | Xuedong Huang | Meng Jiang
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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 applicability. We propose a fact-aware summarization model FASum to extract and integrate factual relations into the summary generation process via graph attention. We then design a factual corrector model FC to automatically correct factual errors from summaries generated by existing systems. Empirical results show that the fact-aware summarization can produce abstractive summaries with higher factual consistency compared with existing systems, and the correction model improves the factual consistency of given summaries via modifying only a few keywords.

2020

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Mixed-Lingual Pre-training for Cross-lingual Summarization
Ruochen Xu | Chenguang Zhu | Yu Shi | Michael Zeng | Xuedong Huang
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

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. translate -> summarize or summarize -> translate. Recently, end-to-end models have achieved better results, but these approaches are mostly limited by their dependence on large-scale labeled data. We propose a solution based on mixed-lingual pre-training that leverages both cross-lingual tasks such as translation and monolingual tasks like masked language models. Thus, our model can leverage the massive monolingual data to enhance its modeling of language. Moreover, the architecture has no task-specific components, which saves memory and increases optimization efficiency. We show in experiments that this pre-training scheme can effectively boost the performance of cross-lingual summarization. In NCLS dataset, our model achieves an improvement of 2.82 (English to Chinese) and 1.15 (Chinese to English) ROUGE-1 scores over state-of-the-art results.

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A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining
Chenguang Zhu | Ruochen Xu | Michael Zeng | Xuedong Huang
Findings of the Association for Computational Linguistics: EMNLP 2020

With the abundance of automatic meeting transcripts, meeting summarization is of great interest to both participants and other parties. Traditional methods of summarizing meetings depend on complex multi-step pipelines that make joint optimization intractable. Meanwhile, there are a handful of deep neural models for text summarization and dialogue systems. However, the semantic structure and styles of meeting transcripts are quite different from articles and conversations. In this paper, we propose a novel abstractive summary network that adapts to the meeting scenario. We design a hierarchical structure to accommodate long meeting transcripts and a role vector to depict the difference among speakers. Furthermore, due to the inadequacy of meeting summary data, we pretrain the model on large-scale news summary data. Empirical results show that our model outperforms previous approaches in both automatic metrics and human evaluation. For example, on ICSI dataset, the ROUGE-1 score increases from 34.66% to 46.28%.

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TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising
Ziyi Yang | Chenguang Zhu | Robert Gmyr | Michael Zeng | Xuedong Huang | Eric Darve
Findings of the Association for Computational Linguistics: EMNLP 2020

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 leverage recurrent neural networks framework while the recently proposed transformer exhibits much more capability. Moreover, most of previous summarization models ignore abundant unlabeled corpora resources available for pretraining. In order to address these issues, we propose TED, a transformer-based unsupervised abstractive summarization system with pretraining on large-scale data. We first leverage the lead bias in news articles to pretrain the model on millions of unlabeled corpora. Next, we finetune TED on target domains through theme modeling and a denoising autoencoder to enhance the quality of generated summaries. Notably, TED outperforms all unsupervised abstractive baselines on NYT, CNN/DM and English Gigaword datasets with various document styles. Further analysis shows that the summaries generated by TED are highly abstractive, and each component in the objective function of TED is highly effective.

2019

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SIM: A Slot-Independent Neural Model for Dialogue State Tracking
Chenguang Zhu | Michael Zeng | Xuedong Huang
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

Dialogue state tracking is an important component in task-oriented dialogue systems to identify users’ goals and requests as a dialogue proceeds. However, as most previous models are dependent on dialogue slots, the model complexity soars when the number of slots increases. In this paper, we put forward a slot-independent neural model (SIM) to track dialogue states while keeping the model complexity invariant to the number of dialogue slots. The model utilizes attention mechanisms between user utterance and system actions. SIM achieves state-of-the-art results on WoZ and DSTC2 tasks, with only 20% of the model size of previous models.

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Multi-task Learning for Natural Language Generation in Task-Oriented Dialogue
Chenguang Zhu | Michael Zeng | Xuedong Huang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

In task-oriented dialogues, Natural Language Generation (NLG) is the final yet crucial step to produce user-facing system utterances. The result of NLG is directly related to the perceived quality and usability of a dialogue system. While most existing systems provide semantically correct responses given goals to present, they struggle to match the variation and fluency in the human language. In this paper, we propose a novel multi-task learning framework, NLG-LM, for natural language generation. In addition to generating high-quality responses conveying the required information, it also explicitly targets for naturalness in generated responses via an unconditioned language model. This can significantly improve the learning of style and variation in human language. Empirical results show that this multi-task learning framework outperforms previous models across multiple datasets. For example, it improves the previous best BLEU score on the E2E-NLG dataset by 2.2%, and on the Laptop dataset by 6.1%.

1994

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Session 2: Language Modeling
Xuedong Huang
Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994

1993

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Efficient Cepstral Normalization for Robust Speech Recognition
Fu-Hua Liu | Richard M. Stern | Xuedong Huang | Alejandro Acero
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

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An Overview of the SPHINX-II Speech Recognition System
Xuedong Huang | Fileno Alleva | Mei-Yuh Hwang | Ronald Rosenfeld
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

1992

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Speech Understanding in Open Tasks
Wayne Ward | Sunil Issar | Xuedong Huang | Hsiao-Wuen Hon | Mei-Yuh Hwang | Sheryl Young | Mike Matessa | Fu-Hua Liu | Richard Stern
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

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Improvements in Stochastic Language Modeling
Ronald Rosenfeld | Xuedong Huang
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

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Subphonetic Modeling for Speech Recognition
Mei-Yuh Hwang | Xuedong Huang
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

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Minimizing Speaker Variation Effects for Speaker-Independent Speech Recognition
Xuedong Huang
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

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Applying SPHINX-II to the DARPA Wall Street Journal CSR Task
F. Alleva | H. Hon | X. Huang | M. Hwang | R. Rosenfeld | R. Weide
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

1991

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A Study on Speaker-Adaptive Speech Recognition
X.D. Huang
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991

1990

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Improved Hidden Markov Modeling for Speaker-Independent Continuous Speech Recognition
Xuedong Huang | Fil Alleva | Satoru Hayamizu | Hsiao-Wuen Hon | Mei-Yuh Hwang | Kai-Fu Lee
Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, Pennsylvania, June 24-27,1990

1989

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Large-Vocabulary Speaker-Independent Continuous Speech Recognition with Semi-Continuous Hidden Markov Models
X.D. Huang | H.W. Hon | K.F. Lee
Speech and Natural Language: Proceedings of a Workshop Held at Cape Cod, Massachusetts, October 15-18, 1989