Segment-based interactive-predictive machine translation

[EN] Machine translation systems require human revision to obtain high-quality translations. Interactive methods provide an efficient human¿computer collaboration, notably increasing productivity. Recently, new interactive protocols have been proposed, seeking for a more effective user interaction with the system. In this work, we present one of these new protocols, which allows the user to validate all correct word sequences in a translation hypothesis. Thus, the left-to-right barrier from most of the existing protocols is broken. We compare this protocol against the classical prefix-based approach, obtaining a significant reduction of the user effort in a simulated environment. Additionally, we experiment with the use of confidence measures to select the word the user should correct at each iteration, reaching the conclusion that the order in which words are corrected does not affect the overall effort.The research leading to these results has received funding from the Ministerio de Economia y Competitividad (MINECO) under Project CoMUN-HaT (Grant Agreement TIN2015-70924-C2-1-R), and Generalitat Valenciana under Project ALMAMATER (Ggrant Agreement PROMETEOII/2014/030).Domingo-Ballester, M.; Peris-Abril, Á.; Casacuberta Nolla, F. (2017). Segment-based interactive-predictive machine translation. Machine Translation. 31(4):163-185. https://doi.org/10.1007/s10590-017-9213-3S163185314Alabau V, Bonk R, Buck C, Carl M, Casacuberta F, García-Martínez M, González-Rubio J, Koehn P, Leiva LA, Mesa-Lao B, Ortiz-Martínez D, Saint-Amand H, Sanchis-Trilles G, Tsoukala C (2013) CASMACAT: an open source workbench for advanced computer aided translation. 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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. 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Implementation of a Human-Computer Interface for Computer Assisted Translation and Handwritten Text Recognition

A human-computer interface is developed to provide services of computer assisted machine translation (CAT) and computer assisted transcription of handwritten text images (CATTI). The back-end machine translation (MT) and handwritten text recognition (HTR) systems are provided by the Pattern Recognition and Human Language Technology (PRHLT) research group. The idea is to provide users with easy to use tools to convert interactive translation and transcription feasible tasks. The assisted service is provided by remote servers with CAT or CATTI capabilities. The interface supplies the user with tools for efficient local edition: deletion, insertion and substitution.Ocampo Sepúlveda, JC. (2009). Implementation of a Human-Computer Interface for Computer Assisted Translation and Handwritten Text Recognition. http://hdl.handle.net/10251/14318Archivo delegad

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

Reinforcement Learning for Machine Translation: from Simulations to Real-World Applications

If a machine translation is wrong, how we can tell the underlying model to fix it? Answering this question requires (1) a machine learning algorithm to define update rules, (2) an interface for feedback to be submitted, and (3) expertise on the side of the human who gives the feedback. This thesis investigates solutions for machine learning updates, the suitability of feedback interfaces, and the dependency on reliability and expertise for different types of feedback. We start with an interactive online learning scenario where a machine translation (MT) system receives bandit feedback (i.e. only once per source) instead of references for learning. Policy gradient algorithms for statistical and neural MT are developed to learn from absolute and pairwise judgments. Our experiments on domain adaptation with simulated online feedback show that the models can largely improve under weak feedback, with variance reduction techniques being very effective. In production environments offline learning is often preferred over online learning. We evaluate algorithms for counterfactual learning from human feedback in a study on eBay product title translations. Feedback is either collected via explicit star ratings from users, or implicitly from the user interaction with cross-lingual product search. Leveraging implicit feedback turns out to be more successful due to lower levels of noise. We compare the reliability and learnability of absolute Likert-scale ratings with pairwise preferences in a smaller user study, and find that absolute ratings are overall more effective for improvements in down-stream tasks. Furthermore, we discover that error markings provide a cheap and practical alternative to error corrections. In a generalized interactive learning framework we propose a self-regulation approach, where the learner, guided by a regulator module, decides which type of feedback to choose for each input. The regulator is reinforced to find a good trade-off between supervision effect and cost. In our experiments, it discovers strategies that are more efficient than active learning and standard fully supervised learning

Regularizing Neural Machine Translation by Target-bidirectional Agreement

Although Neural Machine Translation (NMT) has achieved remarkable progress in the past several years, most NMT systems still suffer from a fundamental shortcoming as in other sequence generation tasks: errors made early in generation process are fed as inputs to the model and can be quickly amplified, harming subsequent sequence generation. To address this issue, we propose a novel model regularization method for NMT training, which aims to improve the agreement between translations generated by left-to-right (L2R) and right-to-left (R2L) NMT decoders. This goal is achieved by introducing two Kullback-Leibler divergence regularization terms into the NMT training objective to reduce the mismatch between output probabilities of L2R and R2L models. In addition, we also employ a joint training strategy to allow L2R and R2L models to improve each other in an interactive update process. Experimental results show that our proposed method significantly outperforms state-of-the-art baselines on Chinese-English and English-German translation tasks.Comment: Accepted by AAAI 201