Sequence-to-sequence neural translation models learn semantic and syntactic relations between sentence pairs by optimizing the likelihood of the target given the source, i.e., @math , an objective that ignores other potentially useful sources of information. We introduce an alternative objective function for neural MT that maximizes the mutual information between the source and target sentences, modeling the bi-directional dependency of sources and targets. We implement the model with a simple re-ranking method, and also introduce a decoding algorithm that increases diversity in the N-best list produced by the first pass. Applied to the WMT German English and French English tasks, the proposed models offers a consistent performance boost on both standard LSTM and attention-based neural MT architectures.
Our goal, distinct from these previous uses of MMI, is to see whether the mutual information objective improves translation by bidirectionally modeling source-target dependencies. In that sense, our work is designed to incorporate into models features that have proved useful in phrase-based MT, like the reverse translation probability or sentence length @cite_0 @cite_8 @cite_5 .
@cite_0 @cite_5 @cite_8
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We use target-side monolingual data to extend the vocabulary of the translation model in statistical machine translation. This method called "reverse self-training" improves the decoder's ability to produce grammatically correct translations into languages with morphology richer than the source language esp. in small-data setting. We empirically evaluate the gains for several pairs of European languages and discuss some approaches of the underlying back-off techniques needed to translate unseen forms of known words. We also provide a description of the systems we submitted to WMT11 Shared Task. In this paper, we introduce a hybrid search for attention-based neural machine translation (NMT). A target phrase learned with statistical MT models extends a hypothesis in the NMT beam search when the attention of the NMT model focuses on the source words translated by this phrase. Phrases added in this way are scored with the NMT model, but also with SMT features including phrase-level translation probabilities and a target language model. Experimental results on German->English news domain and English->Russian e-commerce domain translation tasks show that using phrase-based models in NMT search improves MT quality by up to 2.3 BLEU absolute as compared to a strong NMT baseline. Abstract : We present Minimum Bayes-Risk (MBR) decoding for statistical machine translation. This statistical approach aims to minimize expected loss of translation errors under loss functions that measure translation performance. We describe a hierarchy of loss functions that incorporate different levels of linguistic information from word strings, word-to-word alignments from an MT system, and syntactic structure from parse-trees of source and target language sentences. We report the performance of the MBR decoders on a Chinese-to-English translation task. Our results show that MBR decoding can be used to tune statistical MT performance for specific loss functions. Current syntactic machine translation MT systems implicitly use beam-width unlimited search in learning model parameters e.g., feature values for each translation rule. However, a limited beam-width has to be adopted in decoding new sentences, and the MT output is in general evaluated by various metrics, such as BLEU and TER. In this paper, we address: 1 the mismatch of adopted beam-widths between training and decoding; and 2 the mismatch of training criteria and MT evaluation metrics. Unlike previous work, we model the two problems in a single training paradigm simultaneously. We design a loss-augmented approach that explicitly considers the limited beam-width and evaluation metric in training, and present a simple but effective method to learn the model. By using beam search and BLEU-related losses, our approach improves a state-of-the-art syntactic MT system by +1.0 BLEU on Chinese-to-English and English-to-Chinese translation tasks. It even outperforms seven previous training approaches over 0.8 BLEU points. More interestingly, promising improvements are observed when our approach works with TER.