Abstract
Lexical ambiguity can cause critical failure in conversational spoken language translation (CSLT) systems that rely on statistical machine translation (SMT) if the wrong sense is presented in the target language. Interactive CSLT systems offer the capability to detect and pre-empt such word-sense translation errors (WSTEs) by engaging the human operators in a precise clarification dialogue aimed at resolving the problem. This paper presents an end-to-end framework for accurate detection and interactive resolution of WSTEs to minimize communication errors due to ambiguous source words. We propose (a) a novel, extensible, two-level classification architecture for identifying potential WSTEs in SMT hypotheses; (b) a constrained phrase-pair clustering mechanism for identifying the translated sense of ambiguous source words in SMT hypotheses; and (c) an interactive strategy that integrates this information to request specific clarifying information from the operator. By leveraging unsupervised and lightly supervised learning techniques, our approach minimizes the need for expensive human annotation in developing each component of this framework. Each component, as well as the overall framework, was evaluated in the context of an interactive English-to-Iraqi Arabic CSLT system.
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References
Ananthakrishnan S, Hewavitharana S, Kumar R, Kan E, Prasad R, Natarajan P (2013) Semi-supervised word sense disambiguation for mixed-initiative conversational spoken language translation. In: Sima’an K, Forcada ML, Grasmick D, Depraetere H, Way A (eds) Proceedings of the XIV machine translation summit, Nice, France, pp 43–50, http://www.mt-archive.info/10/MTS-2013-Ananthakrishnan.pdf
Apidianaki M, He Y (2010) An algorithm for cross-lingual sense-clustering tested in a MT evaluation setting. In: Federico M, Lane I, Paul M, Yvon F, Mariani J (eds) Proceedings of the 7th international workshop on spoken language translation, Paris, France, pp 219–226, http://www.mt-archive.info/IWSLT-2010-Apidianaki.pdf
Bach N, Huang F, Al-Onaizan Y (2011) Goodness: a method for measuring machine translation confidence. In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, Portland, Oregon, USA, pp 211–219, http://www.aclweb.org/anthology/P11-1022
Banerjee S, Lavie A (2005) METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, Association for Computational Linguistics, Ann Arbor, Michigan, USA, pp 65–72, http://www.aclweb.org/anthology/W/W05/W05-0909
Bansal M, DeNero J, Lin D (2012) Unsupervised translation sense clustering. In: Proceedings of the 2012 conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, Montréal, Canada, pp 773–782, http://www.aclweb.org/anthology/N12-1095
Berger AL, Della Pietra VJ, Della Pietra SA (1996) A maximum entropy approach to natural language processing. Comput Linguist 22(1):39–71, http://www.aclweb.org/anthology/J/J96/J96-1002.pdf
Bojar O, Buck C, Callison-Burch C, Federmann C, Haddow B, Koehn P, Monz C, Post M, Soricut R, Specia L (2013) Findings of the 2013 workshop on statistical machine translation. In: Proceedings of the 8th workshop on statistical machine translation, Association for Computational Linguistics, Sofia, Bulgaria, pp 1–44, http://www.aclweb.org/anthology/W13-2201
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Callison-Burch C (2007) Paraphrasing and translation. PhD thesis, University of Edinburgh, http://www.cs.jhu.edu/~ccb/publications/thesis.pdf
Carpuat M, Wu D (2005) Word sense disambiguation vs. statistical machine translation. In: Proceedings of the 43rd annual meeting of the Association for Computational Linguistics (ACL’05), Association for Computational Linguistics, Ann Arbor, Michigan, USA, pp 387–394, http://www.aclweb.org/anthology/P05-1048
Carpuat M, Wu D (2007a) How phrase sense disambiguation outperforms word sense disambiguation for statistical machine translation. In: Proceedings of the 11th international conference on theoretical and methodological issues in machine translation, Skövde, Sweden, pp 43–52, http://www.mt-archive.info/TMI-2007-Carpuat.pdf
Carpuat M, Wu D (2007b) Improving statistical machine translation using word sense disambiguation. In: Proceedings of the 2007 Joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL), Association for Computational Linguistics, Prague, Czech Republic, pp 61–72, http://www.aclweb.org/anthology/D/D07/D07-1007.pdf
Chan YS, Ng HT, Chiang D (2007) Word sense disambiguation improves statistical machine translation. In: Proceedings of the 45th annual meeting of the Association of Computational Linguistics, Association for Computational Linguistics, Prague, Czech Republic, pp 33–40, http://www.aclweb.org/anthology/P07-1005
Diab M, Resnik P (2002) An unsupervised method for word sense tagging using parallel corpora. In: Proceedings of 40th annual meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Philadelphia, Pennsylvania, USA, pp 255–262, http://www.aclweb.org/anthology/P02-1033
Gandrabur S, Foster G (2003) Confidence estimation for translation prediction. In: Daelemans W, Osborne M (eds) Proceedings of the 7th conference on natural language learning at HLT-NAACL 2003, Association for Computational Linguistics, Edmonton, Canada, pp 95–102, http://www.aclweb.org/anthology/W03-0413
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explorations 11(1), http://www.kdd.org/sites/default/files/issues/11-1-2009-07/p2V11n1.pdf
Haque R, Naskar SK, van den Bosch A, Way A (2011) Integrating source-language context into phrase-based statistical machine translation. Mach Transl 25(3):239–285
Koehn P (2004) Statistical significance tests for machine translation evaluation. In: Lin D, Wu D (eds) Proceedings of the conference on empirical methods in natural language processing, Association for Computational Linguistics, Barcelona, Spain, pp 388–395, http://www.aclweb.org/anthology/W/W04/W04-3250.pdf
Koehn P, Och FJ, Marcu D (2003) Statistical phrase-based translation. In: Proceedings of the 2003 human language technology conference of the North American Chapter of the Association for Computational Linguistics, Association for Computational Linguistics, Edmonton, Canada, pp 48–54, http://www.aclweb.org/anthology/N/N03/N03-1017.pdf
Koehn P, Hoang H, Birch A, Callison-Burch C, Federico M, Bertoldi N, Cowan B, Shen W, Moran C, Zens R, Dyer C, Bojar O, Constantin A, Herbst E (2007) Moses: open source toolkit for statistical machine translation. In: Proceedings of the 45th annual meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions, Association for Computational Linguistics, Prague, Czech Republic, pp 177–180, http://www.aclweb.org/anthology/P07-2045
Li C, Li H (2002) Word translation disambiguation using bilingual bootstrapping. In: Proceedings of 40th annual meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Philadelphia, Pennsylvania, USA, pp 343–351, http://www.aclweb.org/anthology/P02-1044
Melamed DI (2001) Empirical methods for exploiting parallel texts. The MIT Press, Cambridge
Miller G (1995) Wordnet: a lexical database for english. Communications of The ACM 38:39–41
Ng HT, Wang B, Chan YS (2003) Exploiting parallel texts for word sense disambiguation: an empirical study. In: Proceedings of the 41st annual meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Sapporo, Japan, pp 455–462, http://www.aclweb.org/anthology/P03-1058
Nguyen L, Schwartz R (1997) Efficient 2-pass N-best decoder. DARPA speech recognition workshop. Chantilly, Virginia, USA, pp 167–170
Och FJ (2003) Minimum error rate training in statistical machine translation. In: Proceedings of the 41st annual meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Sapporo, Japan, pp 160–167, http://www.aclweb.org/anthology/P03-1021
Papineni K, Roukos S, Ward T, Zhu WJ (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of 40th annual meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Philadelphia, Pennsylvania, USA, pp 311–318, http://www.aclweb.org/anthology/P02-1040
Purandare A, Pedersen T (2004) Word sense discrimination by clustering contexts in vector and similarity spaces. In: Proceedings of the conference on computational natural language learning (CoNLL), Boston, MA, pp 41–48
Richardson M, Domingos P (2006) Markov logic networks. Mach Learn 62:107–136
Schütze H (1998) Automatic word sense discrimination. Computat Linguist 24(1):97–123, http://www.aclweb.org/anthology/J/J98/J98-1004.pdf
Stroppa N, van den Bosch A, Way A (2007) Exploiting source similarity for SMT using context-informed features. In: TMI-07: Proceedings of The 11th conference on theoretical and methodological issues in machine translation. Skövde, Sweden, pp 231–240
Ueffing N, Ney H (2007) Word-level confidence estimation for machine translation. Computat Linguist 33(1):9–40, http://www.aclweb.org/anthology/J/J07/J07-1003.pdf
Wagstaff K, Cardie C, Rogers S, Schrödl S (2001) Constrained K-means clustering with background knowledge. Proceedings of the 18th international conference on machine learning. Williamstown, Massachusetts, USA, pp 577–584
Yarowsky D (1995) Unsupervised word sense disambiguation rivaling supervised methods. In: Proceedings of the 33rd annual meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Cambridge, Massachusetts, USA, pp 189–196, http://www.aclweb.org/anthology/P95-1026
Acknowledgments
The authors would like to thank the anonymous reviewers for their helpful feedback. This work was funded in part by the DARPA BOLT program under contract number HR0011-12-C-0014. This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA). The views, opinions, and/or findings contained in this article are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. Approved for Public Release, Distribution Unlimited.
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Ananthakrishnan, S., Mehay, D.N., Hewavitharana, S. et al. Lightly supervised word-sense translation-error detection and resolution in an interactive conversational spoken language translation system. Machine Translation 29, 25–47 (2015). https://doi.org/10.1007/s10590-015-9168-1
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DOI: https://doi.org/10.1007/s10590-015-9168-1