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.

Current Members

  • Xing Jie Zhong
    PhD student
  • Darcey Riley
    PhD student
  • Chan Hee (Luke) Song
    Undergraduate student
  • Margareta Rauch
    Undergraduate student
  • Colin McDonald
    Undergraduate student

Former Members


Unsupervised multilingual language learning Models and algorithms for translation, word alignment, and bilingual lexicon induction from parallel and non-parallel texts. Sponsored by DARPA LORELEI and a Google Faculty Research Award.
Neural networks for machine translation Models and algorithms for translation and language modeling using neural networks. Sponsored by an Amazon Academic Research Award and a Google Faculty Research Award.
Documenting endangered languages Technologies for large-scale data collection and automatic transcription and word alignment in endangered and unwritten languages. Sponsored by the National Science Foundation.
Graph grammars for semantics Theory and implementation of grammar formalisms for describing graphs for natural language semantics. Based on the 2014 JHU Workshop on Meaning Representations in Language and Speech Processing.


Midwest Speech and Language Days 2018

Past events

Recent Publications

Toan Q. Nguyen and Julian Salazar. Transformers without tears: improving the normalization of self-attention. In Proc. Workshop on Spoken Language Translation. 2019. doi:10.5281/zenodo.3525484. DOI BibTeX
Kenton Murray, Jeffery Kinnison, Toan Q. Nguyen, Walter Scheirer, and David Chiang. Auto-sizing the Transformer network: improving speed, efficiency, and performance for low-resource machine translation. In Proc. Workshop on Neural Generation and Translation, 231–240. 2019. PDF BibTeX
Arturo Argueta and David Chiang. Accelerating sparse matrix operations in neural networks on graphics processing units. In Proc. ACL, 6215–6224. 2019. PDF BibTeX
Antonios Anastasopoulos, Alison Lui, Toan Q. Nguyen, and David Chiang. Neural machine translation of text from non-native speakers. In Proc. North American Chapter of ACL HLT, volume 1, 3070–3080. 2019. PDF BibTeX
Kenton Murray and David Chiang. Correcting length bias in neural machine translation. In Proc. WMT, 212–223. 2018. PDF BibTeX
Arturo Argueta and David Chiang. Composing finite state transducers on GPUs. In Proc. ACL, 2697–2705. 2018. PDF BibTeX
Justin DeBenedetto and David Chiang. Algorithms and training for weighted multiset automata and regular expressions. In Proc. Implementation and Applications of Automata, 146–158. 2018. PDF BibTeX
Antonios Anastasopoulos and David Chiang. Leveraging translations for speech transcription in low-resource settings. In Proc. INTERSPEECH. 2018. PDF BibTeX
Antonios Anastasopoulos and David Chiang. Tied multitask learning for neural speech translation. In Proc. NAACL HLT, volume 1, 82–91. 2018. PDF BibTeX
Toan Nguyen and David Chiang. Improving lexical choice in neural machine translation. In Proc. NAACL HLT, volume 1, 334–343. 2018. PDF BibTeX
Salvador Aguinaga, David Chiang, and Tim Weninger. Learning hyperedge replacement grammars for graph generation. IEEE Trans. Pattern Analysis and Machine Intelligence, 41(3):625–638, 2019. doi:10.1109/TPAMI.2018.2810877. PDF BibTeX
David Chiang, Frank Drewes, Adam Lopez, and Giorgio Satta. Weighted DAG automata for semantic graphs. Computational Linguistics, 44(1):119–186, 2018. PDF BibTeX
Toan Q. Nguyen and David Chiang. Transfer learning across low-resource, related languages for neural machine translation. In Proc. IJCNLP, volume 2, 296–301. 2017. PDF BibTeX
Antonios Anastasopoulos, Sameer Bansal, David Chiang, Sharon Goldwater, and Adam Lopez. Spoken term discovery for language documentation using translations. In Proc. Workshop on Speech-Centric NLP, 53–58. 2017. PDF BibTeX
Antonios Anastasopoulos and David Chiang. A case study on using speech-to-translation alignments for language documentation. In Proc. Workshop on Use of Computational Methods in Study of Endangered Languages, 170–178. 2017. PDF BibTeX
Huadong Chen, Shujian Huang, David Chiang, and Jiajun Chen. Improved neural machine translation with a syntax-aware encoder and decoder. In Proc. ACL, volume 1, 1936–1945. 2017. PDF BibTeX
Arturo Argueta and David Chiang. Decoding with finite-state transducers on GPUs. In Proc. EACL, volume 1, 1044–1052. 2017. PDF BibTeX
Ulf Hermjakob, Qiang Li, Daniel Marcu, Jonathan May, Sebastian J. Mielke, Nima Pourdamghani, Michael Pust, Xing Shi, Kevin Knight, Tomer Levinboim, Kenton Murray, David Chiang, Boliang Zhang, Xiaoman Pan, Di Lu, Ying Lin, and Heng Ji. Incident-driven machine translation and name tagging for low-resource languages. Machine Translation, 32(1–2):59–89, 2018. doi:10.1007/s10590-017-9207-1. DOI BibTeX

All papers

Language and Computation at Notre Dame