Natural Language Processing at Notre Dame

Natural language processing (NLP) aims to enable computers to use human languages – so that people can, for example, interact with computers naturally; or communicate with people who don't speak a common language; or access speech or text data at scales not otherwise possible. The NLP group at Notre Dame is interested in all aspects of NLP, with a focus on machine translation and connections with formal language theory.

The NLP group co-sponsors NL+, the Natural Language Processing Lunch Seminar.

Current Members

David Chiang

Associate professor

translation, syntax, formal language theory, programming languages
Darcey Riley

PhD student

generation from language models, probabilistic models
Stephen Bothwell

PhD student

NLP for classical languages, computation historical linguistics
Ken Sible

PhD student

retrieval-augmented translation
Aarohi Srivastava

PhD student

dialects, noisy text, zero-shot transfer
Chihiro Taguchi

PhD student

computational linguistics, language documentation, syntax

Former Members

Brian DuSell (PhD 2023 → ETH Zürich)
Colin McDonald (BA 2023 → CMU)
Patrick Soga (BS 2022 → UVA)
Xing Jie Zhong (MS 2021 → Google)
Toan Q. Nguyen (PhD 2021 → Amazon)
Justin DeBenedetto (PhD 2021 → asst. prof. Villanova)
Chan Hee (Luke) Song (BS 2020 → OSU)
Kenton Murray (PhD 2020 → JHU)
Antonios Anastasopoulos (PhD 2019 → postdoc CMU → asst. prof GMU)
Arturo Argueta (PhD 2019 → Apple)
Tomer Levinboim (PhD 2017 → Google)
Xiang Zhou (summer intern 2017 → UNC)
Cindy Xinyi Wang (BS 2017 → PhD CMU → Google)
Ashish Vaswani (PhD 2014 at USC → USC ISI → Google Brain → Adept)

Projects

Expressivity of neural sequence models Relating neural sequence models to automata, grammars, circuits, and logics. Collaboration with Peter Cholak and Anand Pillay.

Retrieval-augmented neural machine translation Augmenting neural machine translation systems by retrieving and using data beyond parallel text. Collaboration with Meng Jiang. Sponsored by NSF.

Natural language (variety) processing Collaboration with Antonis Anastaspoulos (GMU) and Yulia Tsvetkov (UW). Sponsored by NSF.

Language documentation with an AI helper Collaboration with Antonis Anatasopoulos and Geraldine Walther (GMU). Sponsored by NSF.

Differentiable, probabilistic programming with recursive structured models Collaboration with Chung-chieh Shan (IU). Sponsored by NSF.

NLP on medieval texts Analysis of Latin texts and language modeling for OCR of Latin manuscsripts. Collaborations with Walter Scheirer and Hildegund Müller. Sponsored by Notre Dame FRSP.

Recent 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"
}

Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, and others. Beyond the Imitation Game: quantifying and extrapolating the capabilities of language models. 2022. arXiv:2206.04615. PDF BibTeX

@misc{srivastava+:2022,
    author = "Srivastava, Aarohi and Rastogi, Abhinav and Rao, Abhishek and Shoeb, Abu Awal Md and Abid, Abubakar and Fisch, Adam and Brown, Adam R. and Santoro, Adam and Gupta, Aditya and Garriga-Alonso, Adri{\a} and others",
    title = "Beyond the {I}mitation {G}ame: Quantifying and extrapolating the capabilities of language models",
    year = "2022",
    note = "arXiv:2206.04615"
}

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"
}

All papers →

Language and Computation at Notre Dame

Research

People

Meng Jiang: summarization and generation
Toby Li: human-computer interaction
Walter Scheirer: digital humanities and handwriting recognition
John Lalor (ITAO): NLP and biomedical informatics

Courses

CSE 40657/60657, Natural Language Processing, Prof. David Chiang
CSE 40982, Interactive Dialogue Systems, Prof. Collin McMillan
ITAO 40250, Unstructured Data Analytics, Prof. John Lalor
AL 20301, Introduction to Linguistics, Prof. Hana Kang