In this paper, we investigate how new bilingual vocabulary is acquired through Iterative Back-Translation (IBT), which is known as a data augmentation method for machine translation from monolingual data of both source and target languages. To reveal the acquisition process, we first identify the word translation pairs in test data that do not exist in a bilingual data but do only in two monolingual data, then observe how many pairs are successfully translated by the translation model trained through IBT. We experimented on it with domain adaptation settings on two language pairs. Our experimental evaluation showed that more than 60% of the new bilingual vocabulary is successfully acquired through IBT along with the improvement in the translation quality in terms of BLEU. It also revealed that new bilingual vocabulary was gradually acquired by repeating IBT iterations. From the results, we present our hypothesis on the process of new bilingual vocabulary acquisition where the context of the words plays a critical role in the success of the acquisition.

This paper presents NTT-NAIST SMT systems for English-German and German-English MT tasks of the IWSLT 2013 evaluation campaign. The systems are based on generalized minimum Bayes risk system combination of three SMT systems: forest-to-string, hierarchical phrase-based, phrasebased with pre-ordering. Individual SMT systems include data selection for domain adaptation, rescoring using recurrent neural net language models, interpolated language models, and compound word splitting (only for German-English).

Minimum error rate training (MERT) is a widely used learning method for statistical machine translation. In this paper, we present a SVM-based training method to enhance generalization ability. We extend MERT optimization by maximizing the margin between the reference and incorrect translations under the L2-norm prior to avoid overfitting problem. Translation accuracy obtained by our proposed methods is more stable in various conditions than that obtained by MERT. Our experimental results on the French-English WMT08 shared task show that degrade of our proposed methods is smaller than that of MERT in case of small training data or out-of-domain test data.

The NTT Statistical Machine Translation System consists of two primary components: a statistical machine translation decoder and a reranker. The decoder generates k-best translation canditates using a hierarchical phrase-based translation based on synchronous context-free grammar. The decoder employs a linear feature combination among several real-valued scores on translation and language models. The reranker reorders the k-best translation candidates using Ranking SVMs with a large number of sparse features. This paper describes the two components and presents the results for the evaluation campaign of IWSLT 2008.

The NTT Statistical Machine Translation System employs a large number of feature functions. First, k-best translation candidates are generated by an efficient decoding method of hierarchical phrase-based translation. Second, the k-best translations are reranked. In both steps, sparse binary features — of the order of millions — are integrated during the search. This paper gives the details of the two steps and shows the results for the Evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2007.