Bucking the trend: Large-scale cost-focused active learning for statistical machine translation

ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

Volumn , Issue , 2010, Pages 854-864

  Bloodgood, Michael ^a   Callison Burch, Chris ^b  

^a JOHNS HOPKINS UNIVERSITY   (United States)

^b Whiting School of Engineering   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNING; LEARNING STYLES; MACHINE TRANSLATION SYSTEMS; MECHANICAL TURKS; STATISTICAL MACHINE TRANSLATION;

COMPUTATIONAL LINGUISTICS;

EID: 83455164750     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (29)

1. Vamshi Ambati, Stephan Vogel, and Jaime Carbonell. 2010. Active learning and crowd-sourcing for machine translation. In Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10), Valletta, Malta, may. European Language Resources Association (ELRA).
2. Michele Banko and Eric Brill. 2001. Scaling to very very large corpora for natural language disambiguation. In Proceedings of 39th Annual Meeting of the Association for Computational Linguistics, pages 26-33, Toulouse, France, July. Association for Computational Linguistics.
3. Michael Bloodgood and Chris Callison-Burch. 2010. Using mechanical turk to build machine translation evaluation sets. In Proceedings of the Workshop on Creating Speech and Language Data With Amazon's Mechanical Turk, Los Angeles, California, June.Association for Computational Linguistics.
4. Michael Bloodgood and K Vijay-Shanker. 2008. An approach to reducing annotation costs for bionlp. In Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing, pages 104-105, Columbus, Ohio, June. Association for Computational Linguistics.
5. Michael Bloodgood and K Vijay-Shanker. 2009a. A method for stopping active learning based on stabilizing predictions and the need for user-adjustable stopping. In Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009), pages 39-47, Boulder, Colorado, June. Association for Computational Linguistics.
6. Michael Bloodgood and K Vijay-Shanker. 2009b. Taking into account the differences between actively and passively acquired data: The case of active learning with support vector machines for imbalanced datasets. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), pages 137-140, Boulder, Colorado, June. Association for Computational Linguistics.
7. Chris Callison-Burch, Miles Osborne, and Philipp Koehn. 2006. Re-evaluating the role of Bleu in machine translation research. In 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL-2006), Trento, Italy.
8. David Chiang. 2007. Hierarchical phrase-based translation. Computational Linguistics, 33(2):201-228.
9. Ben Hachey, Beatrice Alex, and Markus Becker. 2005. Investigating the effects of selective sampling on the annotation task. In Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005), pages 144-151, Ann Arbor, Michigan, June. Association for Computational Linguistics.
10. Robbie Haertel, Eric Ringger, Kevin Seppi, James Carroll, and Peter McClanahan. 2008. Assessing the costs of sampling methods in active learning for annotation. In Proceedings of ACL-08: HLT, Short Papers, pages 65-68, Columbus, Ohio, June. Association for Computational Linguistics.
11. Gholamreza Haffari and Anoop Sarkar. 2009. Active learning for multilingual statistical machine translation. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pages 181-189, Suntec, Singapore, August. Association for Computational Linguistics.
12. Gholamreza Haffari, Maxim Roy, and Anoop Sarkar. 2009. Active learning for statistical phrase-based machine translation. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 415-423, Boulder, Colorado, June. Association for Computational Linguistics.
13. Rebecca Hwa. 2000. Sample selection for statistical grammar induction. In Hinrich Schütze and Keh-Yih Su, editors, Proceedings of the 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing, pages 45-53. Association for Computational Linguistics, Somerset, New Jersey.
14. Ashish Kapoor, Eric Horvitz, and Sumit Basu. 2007. Selective supervision: Guiding supervised learning with decision-theoretic active learning. In Manuela M. Veloso, editor, IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12, 2007, pages 877-882.
15. Ross D. King, Kenneth E. Whelan, Ffion M. Jones, Philip G. K. Reiser, Christopher H. Bryant,Stephen H. Muggleton, Douglas B. Kell, and Stephen G. Oliver. 2004. Functional genomic hypothesis generation and experimentation by a robot scientist. Nature, 427:247-252, 15 January.
16. David D. Lewis and William A. Gale. 1994. A sequential algorithm for training text classifiers. In SIGIR '94: Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, pages 3-12, New York, NY, USA. Springer-Verlag New York, Inc.
17. Zhifei Li, Chris Callison-Burch, Chris Dyer, Juri Ganitkevitch, Sanjeev Khudanpur, Lane Schwartz, Wren Thornton, JonathanWeese, and Omar Zaidan. 2009. Joshua: An open source toolkit for parsing-based machine translation. In Proceedings of the Fourth Workshop on Statistical Machine Translation, pages 135-139, Athens, Greece, March. Association for Computational Linguistics.
18. Francois Mairesse, Milica Gasic, Filip Jurcicek, Simon Keizer, Jorge Prombonas, Blaise Thomson, Kai Yu, and Steve Young. 2010. Phrase-based statistical language generation using graphical models and active learning. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL), Uppsala, Sweden, July. Association for Computational Linguistics.
19. Grace Ngai and David Yarowsky. 2000. Rule writing or annotation: cost-efficient resource usage for base noun phrase chunking. In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics.
20. Miles Osborne and Jason Baldridge. 2004. Ensemblebased active learning for parse selection. In Daniel Marcu Susan Dumais and Salim Roukos, editors, HLT-NAACL 2004: Main Proceedings, pages 89-96, Boston, Massachusetts, USA, May 2 - May 7. Association for Computational Linguistics.
21. Kishore Papineni, Salim Roukos, ToddWard, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of 40th Annual Meeting of the Association for Computational Linguistics, pages 311-318, Philadelphia, Pennsylvania, USA, July. Association for Computational Linguistics.
22. Manabu Sassano. 2002. An empirical study of active learning with support vector machines for japanese word segmentation. In ACL '02: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pages 505-512, Morristown, NJ, USA. Association for Computational Linguistics.
23. Greg Schohn and David Cohn. 2000. Less is more: Active learning with support vector machines. In Proc. 17th International Conf. on Machine Learning, pages 839-846. Morgan Kaufmann, San Francisco, CA.
24. Rion Snow, Brendan O'Connor, Daniel Jurafsky, and Andrew Ng. 2008. Cheap and fast - but is it good? evaluating non-expert annotations for natural language tasks. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pages 254-263, Honolulu, Hawaii, October.Association for Computational Linguistics.
25. Katrin Tomanek and Udo Hahn. 2009. Semisupervised active learning for sequence labeling. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pages 1039-1047, Suntec, Singapore, August. Association for Computational Linguistics.
26. Simon Tong and Daphne Koller. 2002. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research (JMLR), 2:45-66.
27. David Vickrey, Oscar Kipersztok, and Daphne Koller. 2010. An active learning approach to finding related terms. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL), Uppsala, Sweden, July. Association for Computational Linguistics.
28. Andreas Vlachos. 2008. A stopping criterion for active learning. Computer Speech and Language, 22(3):295-312.
29. Andreas Zollmann and Ashish Venugopal. 2006. Syntax augmented machine translation via chart parsing. In Proceedings of the NAACL-2006 Workshop on Statistical Machine Translation (WMT06), New York, New York.