In order to efficiently improve machine translation systems, we propose a method which selects data to be annotated (manually translated) from speech-to-speech translation field data. For the selection experiments, we used data from field experiments conducted during the 2009 fiscal year in five areas of Japan. For the selection experiments, we used data sets from two areas: one data set giving the lowest baseline speech translation performance for its test set, and another data set giving the highest. In the experiments, we compare two methods for selecting data to be manually translated from the field data. Both of them use source side language models for data selection, but in different manners. According to the experimental results, either or both of the methods show larger improvements compared to a random data selection.

This paper describes the National Institute of Information and Communications Technology/Advanced Telecommunications Research Institute International (NICT/ATR) statistical machine translation (SMT) system used for the IWSLT 2008 evaluation campaign. We participated in the Chinese–English (Challenge Task), English–Chinese (Challenge Task), Chinese–English (BTEC Task), Chinese–Spanish (BTEC Task), and Chinese–English–Spanish (PIVOT Task) translation tasks. In the English–Chinese translation Challenge Task, we focused on exploring various factors for the English–Chinese translation because the research on the translation of English–Chinese is scarce compared to the opposite direction. In the Chinese–English translation Challenge Task, we employed a novel clustering method, where training sentences similar to the development data in terms of the word error rate formed a cluster. In the pivot translation task, we integrated two strategies for pivot translation by linear interpolation.

This paper describes the NiCT-ATR statistical machine translation (SMT) system used for the IWSLT 2007 evaluation campaign. We participated in three of the four language pair translation tasks (CE, JE, and IE). We used a phrase-based SMT system using log-linear feature models for all tracks. This year we decoded from the ASR n-best lists in the JE track and found a gain in performance. We also applied some new techniques to facilitate the use of out-of-domain external resources by model combination and also by utilizing a huge corpus of n-grams provided by Google Inc.. Using these resources gave mixed results that depended on the technique also the language pair however, in some cases we achieved consistently positive results. The results from model-interpolation in particular were very promising.

This paper presents a practical approach to statistical machine translation (SMT) based on syntactic transfer. Conventionally, phrase-based SMT generates an output sentence by combining phrase (multiword sequence) translation and phrase reordering without syntax. On the other hand, SMT based on tree-to-tree mapping, which involves syntactic information, is theoretical, so its features remain unclear from the viewpoint of a practical system. The SMT proposed in this paper translates phrases with hierarchical reordering based on the bilingual parse tree. In our experiments, the best translation was obtained when both phrases and syntactic information were used for the translation process.