David Chiang 蔣偉

Associate Professor, Computer Science and Engineering
Natural Language Processing Group

My research is in natural language processing, the subfield of computer science that aims to enable computers to understand and produce human language. I focus mainly on language translation, and am interested in syntactic parsing and other areas as well.

Teaching

Spring 2023: CSE 30151, Theory of Computing
Fall 2022: CSE 40431, Programming Languages
Fall 2021: CSE 40657/60657, Natural Language Processing
Spring 2021: CSE 40657/60657, Natural Language Processing
Fall 2020: CSE 30151, Theory of Computing

Recent and selected publications

Chihiro Taguchi, Yusuke Sakai, Parisa Haghani, and David Chiang. Universal automatic phonetic transcription into the International Phonetic Alphabet. In Proc. INTERSPEECH. 2023. To appear. PDF BibTeX

@inproceedings{taguchi+:2023,
    author = "Taguchi, Chihiro and Sakai, Yusuke and Haghani, Parisa and Chiang, David",
    title = "Universal Automatic Phonetic Transcription into the {I}nternational {P}honetic {A}lphabet",
    booktitle = "Proc. INTERSPEECH",
    year = "2023",
    note = "To appear"
}

Alexandra Butoi, Ryan Cotterell, and David Chiang. Convergence and diversity in the control hierarchy. In Proc. ACL. 2023. PDF BibTeX

@inproceedings{butoi+:2023,
    author = "Butoi, Alexandra and Cotterell, Ryan and Chiang, David",
    title = "Convergence and Diversity in the Control Hierarchy",
    booktitle = "Proc. ACL",
    year = "2023"
}

David Chiang, Peter Cholak, and Anand Pillay. Tighter bounds on the expressivity of transformer encoders. In Proc. ICML. 2023. PDF BibTeX

@inproceedings{chiang+cholak+pillay:2023,
    author = "Chiang, David and Cholak, Peter and Pillay, Anand",
    title = "Tighter Bounds on the Expressivity of Transformer Encoders",
    booktitle = "Proc. ICML",
    year = "2023"
}

Aarohi Srivastava and David Chiang. Fine-tuning BERT with character-level noise for zero-shot transfer to dialects and closely-related languages. In Proc. Workshop on NLP for Similar Languages, Varieties and Dialects. 2023. PDF BibTeX

@inproceedings{srivastava+chiang:2023fine,
    author = "Srivastava, Aarohi and Chiang, David",
    title = "Fine-Tuning {BERT} with Character-Level Noise for Zero-Shot Transfer to Dialects and Closely-Related Languages",
    year = "2023",
    booktitle = "Proc. Workshop on NLP for Similar Languages, Varieties and Dialects"
}

Patrick Soga and David Chiang. Bridging graph position encodings for transformers with weighted graph-walking automata. Transactions on Machine Learning Research, 2023. PDF BibTeX

@article{soga+chiang:2023,
    author = "Soga, Patrick and Chiang, David",
    title = "Bridging Graph Position Encodings for Transformers with Weighted Graph-Walking Automata",
    year = "2023",
    journal = "Transactions on Machine Learning Research"
}

Brian DuSell and David Chiang. The surprising computational power of nondeterministic stack RNNs. In Proc. ICLR. 2023. PDF BibTeX

@inproceedings{dusell+chiang:2023,
    author = "DuSell, Brian and Chiang, David",
    title = "The Surprising Computational Power of Nondeterministic Stack {RNN}s",
    booktitle = "Proc. ICLR",
    year = "2023"
}

David Chiang, Colin McDonald, and Chung-chieh Shan. Exact recursive probabilistic programming. PACMPL, 2023. doi:10.1145/3586050. PDF BibTeX

@article{chiang+mcdonald+shan:2023,
    author = "Chiang, David and McDonald, Colin and Shan, Chung-chieh",
    title = "Exact Recursive Probabilistic Programming",
    journal = "PACMPL",
    volume = "7",
    number = "OOPSLA1",
    article = "98",
    xmonth = "April",
    year = "2023",
    doi = "10.1145/3586050"
}

Chihiro Taguchi and David Chiang. Introducing morphology in Universal Dependencies Japanese. In Proc. Workshop on Universal Dependencies, 65–72. 2023. PDF BibTeX

@inproceedings{taguchi+chiang:2023,
    author = "Taguchi, Chihiro and Chiang, David",
    title = "Introducing Morphology in {U}niversal {D}ependencies {J}apanese",
    year = "2023",
    booktitle = "Proc. Workshop on Universal Dependencies",
    pages = "65--72"
}

David Chiang, Alexander M. Rush, and Boaz Barak. Named tensor notation. Transactions on Machine Learning Research, 2023. PDF BibTeX

@article{chiang+rush+barak:2023,
    author = "Chiang, David and Rush, Alexander M. and Barak, Boaz",
    title = "Named Tensor Notation",
    year = "2023",
    xmonth = "January",
    journal = "Transactions on Machine Learning Research"
}

Darcey Riley and David Chiang. A continuum of generation tasks for investigating length bias and degenerate repetition. In Proc. BlackboxNLP. 2022. PDF BibTeX

@inproceedings{riley+chiang:2022,
    author = "Riley, Darcey and Chiang, David",
    title = "A Continuum of Generation Tasks for Investigating Length Bias and Degenerate Repetition",
    booktitle = "Proc. BlackboxNLP",
    year = "2022"
}

Alexandra Butoi, Brian DuSell, Tim Vieira, Ryan Cotterell, and David Chiang. Algorithms for weighted pushdown automata. In Proc. EMNLP. 2022. PDF BibTeX

@inproceedings{butoi+:2022,
    author = "Butoi, Alexandra and DuSell, Brian and Vieira, Tim and Cotterell, Ryan and Chiang, David",
    title = "Algorithms for Weighted Pushdown Automata",
    year = "2022",
    booktitle = "Proc. EMNLP"
}

David Chiang and Peter Cholak. Overcoming a theoretical limitation of self-attention. In Proc. ACL. 2022. PDF BibTeX

@inproceedings{chiang+cholak:2022,
    author = "Chiang, David and Cholak, Peter",
    title = "Overcoming a Theoretical Limitation of Self-Attention",
    booktitle = "Proc. ACL",
    year = "2022"
}

Brian DuSell and David Chiang. Learning hierarchical structures with differentiable nondeterministic stacks. In Proc. ICLR. 2022. PDF BibTeX

@inproceedings{dusell+chiang:iclr2022,
    author = "DuSell, Brian and Chiang, David",
    title = "Learning Hierarchical Structures with Differentiable Nondeterministic Stacks",
    booktitle = "Proc. ICLR",
    year = "2022"
}

full list