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.
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Alexandra Butoi, Ryan Cotterell, and David Chiang.
Convergence and diversity in the control hierarchy.
In Proc. ACL. 2023.
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David Chiang, Peter Cholak, and Anand Pillay.
Tighter bounds on the expressivity of transformer encoders.
In Proc. ICML. 2023.
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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.
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Patrick Soga and David Chiang.
Bridging graph position encodings for transformers with weighted graph-walking automata.
Transactions on Machine Learning Research, 2023.
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Brian DuSell and David Chiang.
The surprising computational power of nondeterministic stack RNNs.
In Proc. ICLR. 2023.
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David Chiang, Colin McDonald, and Chung-chieh Shan.
Exact recursive probabilistic programming.
PACMPL, 2023.
doi:10.1145/3586050.
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Chihiro Taguchi and David Chiang.
Introducing morphology in Universal Dependencies Japanese.
In Proc. Workshop on Universal Dependencies, 65–72. 2023.
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David Chiang, Alexander M. Rush, and Boaz Barak.
Named tensor notation.
Transactions on Machine Learning Research, 2023.
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Darcey Riley and David Chiang.
A continuum of generation tasks for investigating length bias and degenerate repetition.
In Proc. BlackboxNLP. 2022.
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Alexandra Butoi, Brian DuSell, Tim Vieira, Ryan Cotterell, and David Chiang.
Algorithms for weighted pushdown automata.
In Proc. EMNLP. 2022.
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David Chiang and Peter Cholak.
Overcoming a theoretical limitation of self-attention.
In Proc. ACL. 2022.
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