Posts by Collection

portfolio

projects

publications

ODSQA: Open-domain Spoken Question Answering Dataset

Published in IEEE Spoken Language Technology(SLT), 2018

Chia-Hsuan Lee, Shang-Ming Wang, Huan-Cheng Chang, Hung-Yi Lee [PDF] [Corpus]


@inproceedings{lee2018odsqa,
  title={Odsqa: Open-domain spoken question answering dataset},
  author={Lee, Chia-Hsuan and Wang, Shang-Ming and Chang, Huan-Cheng and Lee, Hung-Yi},
  booktitle={2018 IEEE Spoken Language Technology Workshop (SLT)},
  pages={949--956},
  year={2018},
  organization={IEEE}
}

Mitigating The Impact Of Speech Recognition Errors On Spoken Question Answering By Adversarial Domain Adaptation

Published in International Conference on Acoustics, Speech, and Signal Processing(ICASSP), 2019

Chia-Hsuan Lee, Yun-Nung Chen, Hung-Yi Lee [PDF]


@inproceedings{lee2019mitigating,
  title={Mitigating the Impact of Speech Recognition Errors on Spoken Question Answering by Adversarial Domain Adaptation},
  author={Lee, Chia-Hsuan and Chen, Yun-Nung and Lee, Hung-Yi},
  booktitle={ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7300--7304},
  year={2019},
  organization={IEEE}
}

Towards Machine Comprehension of Spoken Content

Published in IEEE Transactions on Audio, Speech and Language Processing, 2019

Chia-Hsuan Lee, Hung-yi Lee, Szu-Lin Wu, Chi-Liang Liu, Wei Fang, Juei-Yang Hsu, Bo-Hsiang Tseng[PDF]


@article{lee2019machine,
  title={Machine Comprehension of Spoken Content: TOEFL Listening Test and Spoken SQuAD},
  author={Lee, Chia-Hsuan and Lee, Hung-Yi and Wu, Szu-Lin and Liu, Chi-Liang and Fang, Wei and Hsu, Juei-Yang and Tseng, Bo-Hsiang},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
  year={2019},
  publisher={IEEE}
}

KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers

Published in ACL, 2021

Chia-Hsuan Lee, Oleksandr Polozov, Matthew Richardson [PDF] [Corpus]


@inproceedings{lee-etal-2021-kaggledbqa,
    title = "{K}aggle{DBQA}: Realistic Evaluation of Text-to-{SQL} Parsers",
    author = "Lee, Chia-Hsuan  and
      Polozov, Oleksandr  and
      Richardson, Matthew",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.176",
    doi = "10.18653/v1/2021.acl-long.176",
    pages = "2261--2273",
}

Dialogue State Tracking with a Language Model using Schema-Driven Prompting

Published in EMNLP, 2021

Chia-Hsuan Lee, Hao Cheng, Mari Ostendorf [PDF]


@inproceedings{lee-etal-2021-dialogue,
    title = "Dialogue State Tracking with a Language Model using Schema-Driven Prompting",
    author = "Lee, Chia-Hsuan  and
      Cheng, Hao  and
      Ostendorf, Mari",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.404",
    pages = "4937--4949",
    abstract = "Task-oriented conversational systems often use dialogue state tracking to represent the user{'}s intentions, which involves filling in values of pre-defined slots. Many approaches have been proposed, often using task-specific architectures with special-purpose classifiers. Recently, good results have been obtained using more general architectures based on pretrained language models. Here, we introduce a new variation of the language modeling approach that uses schema-driven prompting to provide task-aware history encoding that is used for both categorical and non-categorical slots. We further improve performance by augmenting the prompting with schema descriptions, a naturally occurring source of in-domain knowledge. Our purely generative system achieves state-of-the-art performance on MultiWOZ 2.2 and achieves competitive performance on two other benchmarks: MultiWOZ 2.1 and M2M. The data and code will be available at https://github.com/chiahsuan156/DST-as-Prompting.",
}

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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