Lattice score based data cleaning for phrase-based statistical machine translation

Statistical machine translation relies heavily on parallel corpora to train its models for translation tasks. While more and more bilingual corpora are readily available, the quality of the sentence pairs should be taken into consideration. This paper presents a novel lattice score-based data cleaning method to select proper sentence pairs from the ones extracted from a bilingual corpus by the sentence alignment methods. The proposed method is carried out as follows: firstly, an initial phrasebased model is trained on the full sentencealigned corpus; then for each of the sentence pairs in the corpus, word alignments are used to create anchor pairs and sourceside lattices; thirdly, based on the translation model, target-side phrase networks are expanded on the lattices and Viterbi searching is used to find approximated decoding results; finally, BLEU score thresholds are used to filter out the low-score sentence pairs for the data cleaning purpose. Our experiments on the FBIS corpus showed improvements of BLEU score from 23.78 to 24.02 in Chinese-English

Competence-based Curriculum Learning for Neural Machine Translation

Current state-of-the-art NMT systems use large neural networks that are not only slow to train, but also often require many heuristics and optimization tricks, such as specialized learning rate schedules and large batch sizes. This is undesirable as it requires extensive hyperparameter tuning. In this paper, we propose a curriculum learning framework for NMT that reduces training time, reduces the need for specialized heuristics or large batch sizes, and results in overall better performance. Our framework consists of a principled way of deciding which training samples are shown to the model at different times during training, based on the estimated difficulty of a sample and the current competence of the model. Filtering training samples in this manner prevents the model from getting stuck in bad local optima, making it converge faster and reach a better solution than the common approach of uniformly sampling training examples. Furthermore, the proposed method can be easily applied to existing NMT models by simply modifying their input data pipelines. We show that our framework can help improve the training time and the performance of both recurrent neural network models and Transformers, achieving up to a 70% decrease in training time, while at the same time obtaining accuracy improvements of up to 2.2 BLEU

Active learning for interactive machine translation

Translation needs have greatly increased during the last years. In many situations, text to be translated constitutes an unbounded stream of data that grows continually with time. An effective approach to translate text documents is to follow an interactive-predictive paradigm in which both the system is guided by the user and the user is assisted by the system to generate error-free translations. Unfortunately, when processing such unbounded data streams even this approach requires an overwhelming amount of manpower. Is in this scenario where the use of active learning techniques is compelling. In this work, we propose different active learning techniques for interactive machine translation. Results show that for a given translation quality the use of active learning allows us to greatly reduce the human effort required to translate the sentences in the stream.González Rubio, J.; Ortiz Martínez, D.; Casacuberta Nolla, F. (2012). Active learning for interactive machine translation. En Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics. 245-254. http://hdl.handle.net/10251/1639524525

Cost-sensitive active learning for computer-assisted translation

This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters, [Volume 37, 1 February 2014, Pages 124–134] DOI: 10.1016/j.patrec.2013.06.007[EN] Machine translation technology is not perfect. To be successfully embedded in real-world applications, it must compensate for its imperfections by interacting intelligently with the user within a computer-assisted translation framework. The interactive¿predictive paradigm, where both a statistical translation model and a human expert collaborate to generate the translation, has been shown to be an effective computer-assisted translation approach. However, the exhaustive supervision of all translations and the use of non-incremental translation models penalizes the productivity of conventional interactive¿predictive systems. We propose a cost-sensitive active learning framework for computer-assisted translation whose goal is to make the translation process as painless as possible. In contrast to conventional active learning scenarios, the proposed active learning framework is designed to minimize not only how many translations the user must supervise but also how difficult each translation is to supervise. To do that, we address the two potential drawbacks of the interactive-predictive translation paradigm. On the one hand, user effort is focused to those translations whose user supervision is considered more ¿informative¿, thus, maximizing the utility of each user interaction. On the other hand, we use a dynamic machine translation model that is continually updated with user feedback after deployment. We empirically validated each of the technical components in simulation and quantify the user effort saved. We conclude that both selective translation supervision and translation model updating lead to important user-effort reductions, and consequently to improved translation productivity.Work supported by the European Union Seventh Framework Program (FP7/2007-2013) under the CasMaCat Project (Grants agreement No. 287576), by the Generalitat Valenciana under Grant ALMPR (Prometeo/2009/014), and by the Spanish Government under Grant TIN2012-31723. The authors thank Daniel Ortiz-Martinez for providing us with the log-linear SMT model with incremental features and the corresponding online learning algorithms. The authors also thank the anonymous reviewers for their criticisms and suggestions.González Rubio, J.; Casacuberta Nolla, F. (2014). Cost-sensitive active learning for computer-assisted translation. Pattern Recognition Letters. 37(1):124-134. https://doi.org/10.1016/j.patrec.2013.06.007S12413437