Online learning via dynamic reranking for Computer Assisted Translation

New techniques for online adaptation in computer assisted translation are explored and compared to previously existing approaches. Under the online adaptation paradigm, the translation system needs to adapt itself to real-world changing scenarios, where training and tuning may only take place once, when the system is set-up for the first time. For this purpose, post-edit information, as described by a given quality measure, is used as valuable feedback within a dynamic reranking algorithm. Two possible approaches are presented and evaluated. The first one relies on the well-known perceptron algorithm, whereas the second one is a novel approach using the Ridge regression in order to compute the optimum scaling factors within a state-of-the-art SMT system. Experimental results show that such algorithms are able to improve translation quality by learning from the errors produced by the system on a sentence-by-sentence basis.This paper is based upon work supported by the EC (FEDER/FSE) and the Spanish MICINN under projects MIPRCV “Consolider Ingenio 2010” (CSD2007-00018) and iTrans2 (TIN2009-14511). Also supported by the Spanish MITyC under the erudito.com (TSI-020110-2009-439) project, by the Generalitat Valenciana under grant Prometeo/2009/014 and scholarship GV/2010/067 and by the UPV under grant 20091027Martínez Gómez, P.; Sanchis Trilles, G.; Casacuberta Nolla, F. (2011). Online learning via dynamic reranking for Computer Assisted Translation. En Computational Linguistics and Intelligent Text Processing. Springer Verlag (Germany). 6609:93-105. https://doi.org/10.1007/978-3-642-19437-5_8S931056609Brown, P., Pietra, S.D., Pietra, V.D., Mercer, R.: The mathematics of machine translation. In: Computational Linguistics, vol. 19, pp. 263–311 (1993)Zens, R., Och, F.J., Ney, H.: Phrase-based statistical machine translation. In: Jarke, M., Koehler, J., Lakemeyer, G. (eds.) KI 2002. LNCS (LNAI), vol. 2479, pp. 18–32. Springer, Heidelberg (2002)Koehn, P., Och, F.J., Marcu, D.: Statistical phrase-based translation. In: Proc. HLT/NAACL 2003, pp. 48–54 (2003)Callison-Burch, C., Fordyce, C., Koehn, P., Monz, C., Schroeder, J.: (meta-) evaluation of machine translation. In: Proc. of the Workshop on SMT. ACL, pp. 136–158 (2007)Papineni, K., Roukos, S., Ward, T.: Maximum likelihood and discriminative training of direct translation models. In: Proc. of ICASSP 1988, pp. 189–192 (1998)Och, F., Ney, H.: Discriminative training and maximum entropy models for statistical machine translation. In: Proc. of the ACL 2002, pp. 295–302 (2002)Och, F., Zens, R., Ney, H.: Efficient search for interactive statistical machine translation. In: Proc. of EACL 2003, pp. 387–393 (2003)Sanchis-Trilles, G., Casacuberta, F.: Log-linear weight optimisation via bayesian adaptation in statistical machine translation. In: Proceedings of COLING 2010, Beijing, China (2010)Callison-Burch, C., Bannard, C., Schroeder, J.: Improving statistical translation through editing. In: Proc. of 9th EAMT Workshop Broadening Horizons of Machine Translation and its Applications, Malta (2004)Barrachina, S., et al.: Statistical approaches to computer-assisted translation. Computational Linguistics 35, 3–28 (2009)Casacuberta, F., et al.: Human interaction for high quality machine translation. Communications of the ACM 52, 135–138 (2009)Ortiz-Martínez, D., García-Varea, I., Casacuberta, F.: Online learning for interactive statistical machine translation. In: Proceedings of NAACL HLT, Los Angeles (2010)España-Bonet, C., Màrquez, L.: Robust estimation of feature weights in statistical machine translation. In: 14th Annual Conference of the EAMT (2010)Reverberi, G., Szedmak, S., Cesa-Bianchi, N., et al.: Deliverable of package 4: Online learning algorithms for computer-assisted translation (2008)Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. Journal of Machine Learning Research 7, 551–585 (2006)Snover, M., Dorr, B., Schwartz, R., Micciulla, L., Makhoul, J.: A study of translation edit rate with targeted human annotation. In: Proc. of AMTA, Cambridge, MA, USA (2006)Papineni, K., Roukos, S., Ward, T., Zhu, W.: Bleu: A method for automatic evaluation of machine translation. In: Proc. of ACL 2002 (2002)Rosenblatt, F.: The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65, 386–408 (1958)Collins, M.: Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. In: EMNLP 2002, Philadelphia, PA, USA, pp. 1–8 (2002)Koehn, P.: Europarl: A parallel corpus for statistical machine translation. In: Proc. of the MT Summit X, pp. 79–86 (2005)Koehn, P., et al.: Moses: Open source toolkit for statistical machine translation. In: Proc. of the ACL Demo and Poster Sessions, Prague, Czech Republic, pp. 177–180 (2007)Och, F.: Minimum error rate training for statistical machine translation. In: Proc. of ACL 2003, pp. 160–167 (2003)Kneser, R., Ney, H.: Improved backing-off for m-gram language modeling. In: IEEE Int. Conf. on Acoustics, Speech and Signal Processing II, pp. 181–184 (1995)Stolcke, A.: SRILM – an extensible language modeling toolkit. In: Proc. of ICSLP 2002, pp. 901–904 (2002

Fast Reinforcement Learning with Large Action Sets Using Error-Correcting Output Codes for MDP Factorization

International audienceThe use of Reinforcement Learning in real-world scenarios is strongly limited by issues of scale. Most RL learning algorithms are unable to deal with problems composed of hundreds or sometimes even dozens of possible actions, and therefore cannot be applied to many real-world problems. We consider the RL problem in the supervised classification framework where the optimal policy is obtained through a multiclass classifier, the set of classes being the set of actions of the problem. We introduce error-correcting output codes (ECOCs) in this setting and propose two new methods for reducing complexity when using rollouts-based approaches. The first method consists in using an ECOC-based classifier as the multiclass classifier, reducing the learning complexity from O(A2) to O(Alog(A)) . We then propose a novel method that profits from the ECOC's coding dictionary to split the initial MDP into O(log(A)) separate two-action MDPs. This second method reduces learning complexity even further, from O(A2) to O(log(A)) , thus rendering problems with large action sets tractable. We finish by experimentally demonstrating the advantages of our approach on a set of benchmark problems, both in speed and performance

Learning Economic Parameters from Revealed Preferences

A recent line of work, starting with Beigman and Vohra (2006) and Zadimoghaddam and Roth (2012), has addressed the problem of {\em learning} a utility function from revealed preference data. The goal here is to make use of past data describing the purchases of a utility maximizing agent when faced with certain prices and budget constraints in order to produce a hypothesis function that can accurately forecast the {\em future} behavior of the agent. In this work we advance this line of work by providing sample complexity guarantees and efficient algorithms for a number of important classes. By drawing a connection to recent advances in multi-class learning, we provide a computationally efficient algorithm with tight sample complexity guarantees ($\Theta(d/\epsilon)$ for the case of $d$ goods) for learning linear utility functions under a linear price model. This solves an open question in Zadimoghaddam and Roth (2012). Our technique yields numerous generalizations including the ability to learn other well-studied classes of utility functions, to deal with a misspecified model, and with non-linear prices

Efficient Feature Selection and Multiclass Classification with Integrated Instance and Model Based Learning

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BDUOL: Double Updating Online Learning on a Fixed Budget

Ministry of Education, Singapore under its Academic Research Funding Tier 1; Microsoft Research gran

ImageNet Large Scale Visual Recognition Challenge

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