Mei-Yuh Hwang

Also published as: M. Hwang


2019

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Incremental Learning from Scratch for Task-Oriented Dialogue Systems
Weikang Wang | Jiajun Zhang | Qian Li | Mei-Yuh Hwang | Chengqing Zong | Zhifei Li
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Clarifying user needs is essential for existing task-oriented dialogue systems. However, in real-world applications, developers can never guarantee that all possible user demands are taken into account in the design phase. Consequently, existing systems will break down when encountering unconsidered user needs. To address this problem, we propose a novel incremental learning framework to design task-oriented dialogue systems, or for short Incremental Dialogue System (IDS), without pre-defining the exhaustive list of user needs. Specifically, we introduce an uncertainty estimation module to evaluate the confidence of giving correct responses. If there is high confidence, IDS will provide responses to users. Otherwise, humans will be involved in the dialogue process, and IDS can learn from human intervention through an online learning module. To evaluate our method, we propose a new dataset which simulates unanticipated user needs in the deployment stage. Experiments show that IDS is robust to unconsidered user actions, and can update itself online by smartly selecting only the most effective training data, and hence attains better performance with less annotation cost.

2018

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A Teacher-Student Framework for Maintainable Dialog Manager
Weikang Wang | Jiajun Zhang | Han Zhang | Mei-Yuh Hwang | Chengqing Zong | Zhifei Li
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Reinforcement learning (RL) is an attractive solution for task-oriented dialog systems. However, extending RL-based systems to handle new intents and slots requires a system redesign. The high maintenance cost makes it difficult to apply RL methods to practical systems on a large scale. To address this issue, we propose a practical teacher-student framework to extend RL-based dialog systems without retraining from scratch. Specifically, the “student” is an extended dialog manager based on a new ontology, and the “teacher” is existing resources used for guiding the learning process of the “student”. By specifying constraints held in the new dialog manager, we transfer knowledge of the “teacher” to the “student” without additional resources. Experiments show that the performance of the extended system is comparable to the system trained from scratch. More importantly, the proposed framework makes no assumption about the unsupported intents and slots, which makes it possible to improve RL-based systems incrementally.

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Source Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language
He Bai | Yu Zhou | Jiajun Zhang | Liang Zhao | Mei-Yuh Hwang | Chengqing Zong
Proceedings of the 27th International Conference on Computational Linguistics

To deploy a spoken language understanding (SLU) model to a new language, language transferring is desired to avoid the trouble of acquiring and labeling a new big SLU corpus. An SLU corpus is a monolingual corpus with domain/intent/slot labels. Translating the original SLU corpus into the target language is an attractive strategy. However, SLU corpora consist of plenty of semantic labels (slots), which general-purpose translators cannot handle well, not to mention additional culture differences. This paper focuses on the language transferring task given a small in-domain parallel SLU corpus. The in-domain parallel corpus can be used as the first adaptation on the general translator. But more importantly, we show how to use reinforcement learning (RL) to further adapt the adapted translator, where translated sentences with more proper slot tags receive higher rewards. Our reward is derived from the source input sentence exclusively, unlike reward via actor-critical methods or computing reward with a ground truth target sentence. Hence we can adapt the translator the second time, using the big monolingual SLU corpus from the source language. We evaluate our approach on Chinese to English language transferring for SLU systems. The experimental results show that the generated English SLU corpus via adaptation and reinforcement learning gives us over 97% in the slot F1 score and over 84% accuracy in domain classification. It demonstrates the effectiveness of the proposed language transferring method. Compared with naive translation, our proposed method improves domain classification accuracy by relatively 22%, and the slot filling F1 score by relatively more than 71%.

2016

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Recurrent Support Vector Machines For Slot Tagging In Spoken Language Understanding
Yangyang Shi | Kaisheng Yao | Hu Chen | Dong Yu | Yi-Cheng Pan | Mei-Yuh Hwang
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Leave-One-Out Phrase Model Training for Large-Scale Deployment
Joern Wuebker | Mei-Yuh Hwang | Chris Quirk
Proceedings of the Seventh Workshop on Statistical Machine Translation

2010

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Thai Sentence-Breaking for Large-Scale SMT
Glenn Slayden | Mei-Yuh Hwang | Lee Schwartz
Proceedings of the 1st Workshop on South and Southeast Asian Natural Language Processing

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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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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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

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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Recent Progress in the Sphinx Speech Recognition System
Kai-Fu Lee | Hsiao-Wuen Hon | Mei-Yuh Hwang
Speech and Natural Language: Proceedings of a Workshop Held at Philadelphia, Pennsylvania, February 21-23, 1989