How can we enable computers to understand human languages?

Professor Chris Callison-Burch is a computer scientist who studies machine translation.

Some of the fundamental questions that Professor Trueswell is interested in include:

How can computers to identify meaning-equivalent English expressions even when they are worded differently?
Can we build models to represent the meaning of individual words and how their meaning changes as they are composed into longer phrases and sentences?
How can we build better machine translation systems that are able to translate more of the world’s languages?
Can non-linguistic data like images be used to help computers understand language?

Currently, Professor Callison-Burch has three areas of research. His primary research focus is to automate the understanding of English via paraphrases. He adapt the data, representations, and algorithms from statistical machine translation to facilitate natural language understanding. In addition, Professor Callison-Burch has two other research directions. One attempts to extend machine translation so that it may be applied to a wider range of languages by doing away with the necessity for bilingual parallel corpora. Instead, my research focuses on learning translations from monolingual texts in two languages. His third research focus is on using crowdsourcing to explore new areas of natural language processing. (This crowdsourcing work has even extended beyond NLP and now includes social justice issues, including workers’ rights and gun violence in the United States.)

Professor Callison-Burch is an associate professor of Computer and Information Science at the University of Pennsylvania. Before joining Penn, he was a research faculty member at the Center for Language and Speech Processing at Johns Hopkins University for 6 years. He served as the General Chair of the ACL 2017 conference, and the Program Co-Chair for the EMNLP 2015 conference. He has served as the Chair of the NAACL Executive Board, the Secretary-Treasurer for SIGDAT, and on the editorial boards of TACL and Computational Linguistics. He has more than 100 publications, which have been cited over 14,000 times. He is a Sloan Research Fellow, and he has received faculty research awards from Google, Microsoft, Amazon and Facebook in addition to funding from DARPA and the NSF.

Chris Callison-Burch

Associate Professor of Computer and Information Science

3401 Walnut, room 463C

ccb@cis.upenn.edu

Personal website

Selected Publications

Bannard, C., & Callison-Burch, C. (2005, June). Paraphrasing with bilingual parallel corpora. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (pp. 597-604). Association for Computational Linguistics.

Ganitkevitch, J., Van Durme, B., & Callison-Burch, C. (2013). PPDB: The paraphrase database. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 758-764).

Pavlick, E., Bos, J., Nissim, M., Beller, C., Van Durme, B., & Callison-Burch, C. (2015). Adding semantics to data-driven paraphrasing. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Vol. 1, pp. 1512-1522).

Cocos, A., & Callison-Burch, C. (2016). Clustering paraphrases by word sense. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 1463-1472).

Hewitt, J., Ippolito, D., Callahan, B., Kriz, R., Wijaya, D. T., & Callison-Burch, C. (2018). Learning translations via images with a massively multilingual image dataset. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Vol. 1, pp. 2566-2576).