We apply phrase-based and neural models to a core task in interactive machine translation: suggesting how to complete a partial translation.For the phrase-based system, we demonstrate improvements in suggestion quality using novel objective functions, learning techniques, and inference algorithms tailored to this task.Our contributions include new tunable metrics, an improved beam search strategy, an n-best extraction method that increases suggestion diversity, and a tuning procedure for a hierarchical joint model of alignment and translation.The combination of these techniques improves next-word suggestion accuracy dramatically from 28.5% to 41.2% in a large-scale English-German experiment.Our recurrent neural translation system increases accuracy yet further to 53.0%, but inference is two orders of magnitude slower.Manual error analysis shows the strengths and weaknesses of both approaches.

We use character-based statistical machine translation in order to correct user search queries in the e-commerce domain.The training data is automatically extracted from event logs where users re-issue their search queries with potentially corrected spelling within the same session.We show results on a test set which was annotated by humans and compare against online autocorrection capabilities of three additional web sites.Overall, the methods presented in this paper outperform fully productized spellchecking and autocorrection services in terms of accuracy and F1 score.We also propose novel evaluation steps based on retrieved search results of the corrected queries in terms of quantity and relevance.

We summarize recent progress on SRI's IraqCommtrade Iraqi Arabic-English two-way speech-to-speech translation system. In the past year we made substantial developments in our speech recognition and machine translation technology, leading to significant improvements in both accuracy and speed of the IraqComm system. On the 2008 NIST-evaluation dataset our twoway speech-to-text (S2T) system achieved 6% to 8% absolute improvement in BLEU in both directions, compared to our previous year system.

We present the RWTH phrase-based statistical machine translation system designed for the translation of Arabic speech into English text. This system was used in the Global Autonomous Language Exploitation (GALE) Go/No-Go Translation Evaluation 2007. Using a two-pass approach, we first generate n-best translation candidates and then rerank these candidates using additional models. We give a short review of the decoder as well as of the models used in both passes. We stress the difficulties of spoken language translation, i.e. how to combine the recognition and translation systems and how to compensate for missing punctuation. In addition, we cover our work on domain adaptation for the applied language models. We present translation results for the official GALE 2006 evaluation set and the GALE 2007 development set.

In this paper we present the ongoing work at RWTH Aachen University for building a speech-to-speech translation system within the TC-Star project. The corpus we work on consists of parliamentary speeches held in the European Plenary Sessions. To our knowledge, this is the first project that focuses on speech-to-speech translation applied to a real-life task. We describe the statistical approach used in the development of our system and analyze its performance under different conditions: dealing with syntactically correct input, dealing with the exact transcription of speech and dealing with the (noisy) output of an automatic speech recognition system. Experimental results show that our system is able to perform adequately in each of these conditions.