Chris Quirk
Researcher, Natural Language Processing Group
After studying Computer Science and Mathematics at Carnegie Mellon University, he joined Microsoft in 2000, and started work with the Natural Language Processing group in 2001. Currently his focus is machine translation, exploring syntactic approaches for statistical machine translation. In addition, he has investigated automated MT evaluation, translation confidence scoring, and applying effective statistical machine translation techniques to monolingual problems such as paraphrase.
Publications
2008
- Colin Cherry and Chris Quirk, Discriminative, Syntactic Language Modeling through Latent SVMs, in Proceeding of AMTA, Association for Machine Translation in the Americas, 23 October 2008
- Menezes, Arul, Quirk, and Chris, Syntactic Models for Structural Word Insertion and Deletion during Translation, in Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Honolulu, Hawaii, October 2008
- Moore, Robert C., Quirk, and Chris, Random Restarts in Minimum Error Rate Training for Statistical Machine Translation, in Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), Coling 2008 Organizing Committee, Manchester, UK, August 2008
- Zhang, Hao, Quirk, Chris, Moore, Robert C., Gildea, and Daniel, Bayesian Learning of Non-Compositional Phrases with Synchronous Parsing, in Proceedings of ACL-08: HLT, Association for Computational Linguistics, Columbus, Ohio, June 2008
2007
- Robert C. Moore and Chris Quirk, Faster Beam-Search Decoding for Phrasal Statistical Machine Translation, in Proceedings of MT Summit XI, European Association for Machine Translation, September 2007
- Chris Quirk, Raghavendra Udupa, and Arul Menezes, Generative Models of Noisy Translations with Applications to Parallel Fragment Extraction, in Proceedings of MT Summit XI, European Association for Machine Translation, September 2007
- Robert C. Moore and Chris Quirk, An Iteratively-Trained Segmentation-Free Phrase Translation Model for Statistical Machine Translation, in Proceedings of the Second Workshop on Statistical Machine Translation at ACL 2007, Association for Computational Linguistics, July 2007
2006
- Chris Quirk and Simon Corston-Oliver, The impact of parse quality on syntactically-informed statistical machine translation, in Proceedings of EMNLP 2006, ACL/SIGPARSE, July 2006
- Chris Quirk and Arul Menezes, Do we need phrases? Challenging the conventional wisdom in Statistical Machine Translation, in Proceedings of HLT-NAACL 2006, ACL/SIGPARSE, May 2006
- Xiaodong He, Arul Menezes, Chris Quirk, Anthony Aue, Simon Corston-Oliver, Jianfeng Gao, and Patrick Nguyen, Microsoft Research Treelet Translation System: NIST MT Evaluation 06, National Institute of Standards and Technology , March 2006
- Chris Quirk and Arul Menezes, Dependency Treelet Translation: The convergence of statistical and example-based machine translation?, in Machine Translation, vol. 20, pp. 43–65, March 2006
2005
- Arul Menezes and Chris Quirk, Microsoft Research Treelet Translation System: IWSLT Evaluation, in Proceedings of the International Workshop on Spoken Language Translation, October 2005
- Chris Quirk, Arul Menezes, and Colin Cherry, Dependency Treelet Translation: Syntactically Informed Phrasal SMT, in Proceedings of ACL, Association for Computational Linguistics, June 2005
2004
- Chris Quirk, Arul Menezes, and Colin Cherry, Dependency Tree Translation: Syntactically Informed Phrasal SMT, no. MSR-TR-2004-113, November 2004
- Anthony Aue, Arul Menezes, Robert Moore, Chris Quirk, and Eric Ringger, Statistical Machine Translation Using Labeled Semantic Dependency Graphs, ACL/SIGPARSE, October 2004
- William Dolan, Chris Quirk, and Chris Brockett, Unsupervised Construction of Large Paraphrase Corpora: Exploiting Massively Parallel News Sources, International Conference on Computational Linguistics, August 2004
- Chris Quirk, Chris Brockett, and William B. Dolan, Monolingual Machine Translation for Paraphrase Generation, Association for Computational Linguistics, July 2004
- Chris Quirk, Training a Sentence-Level Machine Translation Confidence Measure, European Language Resources Association, May 2004




