Hideo Okuma

Introducing a Translation Dictionary into Phrase-Based SMT

This paper presents a method to effectively introduce a translation dictionary into phrase-based SMT. Though SMT systems can be built with only a parallel corpus, translation dictionaries are more widely available and have many more entries than parallel corpora. A simple and low-cost method to introduce a translation dictionary is to attach a dictionary entry into a phrase table. This, however, does not work well. Target word order and even whole target sentences are often incorrect. To solve this problem, the proposed method uses high-frequency words in the training corpus. The high-frequency words may already be trained well; in other words, they may appear in the phrase table and therefore be translated with correct word order. Experimental results show the proposed method as far superior to simply attaching dictionary entries into phrase tables.

Multilingual Speech-to-Speech Translation System: VoiceTra

This study presents an overview of VoiceTra, which was developed by NICT and released as the worlds first network-based multilingual speech-to-speech translation system for smartphones, and describes in detail its multilingual speech recognition, its multilingual translation, and its multilingual speech synthesis in regards to field experiments. We show the effects of system updates using the data collected from field experiments to improve our acoustic and language models.

Reordering Model Using Syntactic Information of a Source Tree for Statistical Machine Translation

This paper presents a reordering model using syntactic information of a source tree for phrase-based statistical machine translation. The proposed model is an extension of ISTITG (imposing source tree on inversion transduction grammar) constraints. In the proposed method, the target-side word order is obtained by rotating nodes of the source-side parse-tree. We modeled the node rotation, monotone or swap, using word alignments based on a training parallel corpus and sourceside parse-trees. The model efficiently suppresses erroneous target word orderings, especially global orderings. Furthermore, the proposed method conducts a probabilistic evaluation of target word reorderings. In English-to-Japanese and English-to-Chinese translation experiments, the proposed method resulted in a 0.49-point improvement (29.31 to 29.80) and a 0.33-point improvement (18.60 to 18.93) in word BLEU-4 compared with IST-ITG constraints, respectively. This indicates the validity of the proposed reordering model.