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.

People

Projects

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.

Events

2018/05/10–11
Midwest Speech and Language Days 2018
2017/12/07
Talk: Taylor Berg-Kirkpatrick (Carnegie Mellon University)
2017/11/10
Talk: Kyunghyun Cho (New York University)
2017/11/09
Talk: Karl Stratos (Toyota Technological Institute at Chicago)

Past events

Recent Publications

Xinyi Wang, Salvador Aguinaga, Tim Weninger, and David Chiang. Growing better graphs with latent-variable probabilistic graph grammars. In Proc. Workshop on Mining and Learning with Grammars. 2018. To appear. PDF BibTeX
Antonios Anastasopoulos, Marika Lekakou, Josep Quer, Eleni Zimianiti, Justin DeBenedetto, and David Chiang. Part-of-speech tagging on an endangered language: a parallel Griko-Italian resource. In Proc. COLING. 2018. To appear. PDF BibTeX
Arturo Argueta and David Chiang. Composing finite state transducers on GPUs. In Proc. ACL. 2018. To appear. PDF BibTeX
Justin DeBenedetto and David Chiang. Algorithms and training for weighted multiset automata and regular expressions. In Proc. Implementation and Applications of Automata. 2018. To appear. PDF BibTeX
Antonios Anastasopoulos and David Chiang. Leveraging translations for speech transcription in low-resource settings. In Proc. INTERSPEECH. 2018. To appear. PDF BibTeX
Corey Pennycuff, Satyaki Sikdar, Catalina Vajiac, David Chiang, and Tim Weninger. Synchronous hyperedge replacement graph grammars. In Proc. Graph Transformations. 2018. To appear. BibTeX
Antonios Anastasopoulos and David Chiang. Tied multitask learning for neural speech translation. In Proc. NAACL HLT, 82–91. 2018. PDF BibTeX
Toan Nguyen and David Chiang. Improving lexical choice in neural machine translation. In Proc. NAACL HLT, 334–343. 2018. PDF BibTeX
Huadong Chen, Shujian Huang, David Chiang, Xinyu Dai, and Jiajun Chen. Combining character and word information in neural machine translation using a multi-level attention. In Proc. NAACL HLT, 1284–1293. 2018. PDF BibTeX
Tim Weninger Salvador Aguinaga, David Chiang. Learning hyperedge replacement grammars for graph generation. IEEE Trans. Pattern Analysis and Machine Intelligence, 2018. To appear. 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

All papers

Language and Computation at Notre Dame

Research

People

Courses