State-of-the-art statistical machine translation systems use hypotheses from several maximum a posteriori inference steps, including word alignments and parse trees, to identify translational structure and estimate the parameters of translation models. While this approach leads to a modular pipeline of independently developed components, errors made in these “single-best” hypotheses can propagate to downstream estimation steps that treat these inputs as clean, trustworthy training data. In this work we integrate N-best alignments and parses by using a probability distribution over these alternatives to generate posterior fractional counts for use in downstream estimation. Using these fractional counts in a DOP-inspired syntax-based translation system, we show significant improvements in translation quality over a single-best trained baseline.

We present the CMU Syntax Augmented Machine Translation System that was used in the IWSLT-08 evaluation campaign. We participated in the Full-BTEC data track for Chinese-English translation, focusing on transcript translation. For this year’s evaluation, we ported the Syntax Augmented MT toolkit [1] to the Hadoop MapReduce [2] parallel processing architecture, allowing us to efficiently run experiments evaluating a novel “wider pipelines” approach to integrate evidence from N -best alignments into our translation models. We describe each step of the MapReduce pipeline as it is implemented in the open-source SAMT toolkit, and show improvements in translation quality by using N-best alignments in both hierarchical and syntax augmented translation systems.

This paper describes the CMU-UKA statistical machine translation systems submitted to the IWSLT 2007 evaluation campaign. Systems were submitted for three language-pairs: Japanese→English, Chinese→English and Arabic→English. All systems were based on a common phrase-based SMT (statistical machine translation) framework but for each language-pair a specific research problem was tackled. For Japanese→English we focused on two problems: first, punctuation recovery, and second, how to incorporate topic-knowledge into the translation framework. Our Chinese→English submission focused on syntax-augmented SMT and for the Arabic→English task we focused on incorporating morphological-decomposition into the SMT framework. This research strategy enabled us to evaluate a wide variety of approaches which proved effective for the language pairs they were evaluated on.