Matthias Paulik

This paper describes an approach for computing a consensus translation from the outputs of multiple machine translation (MT) systems. The consensus translation is computed by weighted majority voting on a confusion network, similarly to the well-established ROVER approach of Fiscus for combining speech recognition hypotheses. To create the confusion network, pairwise word alignments of the original MT hypotheses are learned using an enhanced statistical alignment algorithm that explicitly models word reordering. The context of a whole corpus of automatic translations rather than a single sentence is taken into account in order to achieve high alignment quality. The confusion network is rescored with a special language model, and the consensus translation is extracted as the best path. The proposed system combination approach was evaluated in the framework of the TC-STAR speech translation project. Up to six state-of-the-art statistical phrase-based translation systems from different project partners were combined in the experiments. Significant improvements in translation quality from Spanish to English and from English to Spanish in comparison with the best of the individual MT systems were achieved under official evaluation conditions.

In this paper we describe our work in coupling automatic speech recognition (ASR) and machine translation (MT) in a speech translation enhanced automatic speech recognition (STE-ASR) framework for transcribing and translating European parliament speeches. We demonstrate the influence of the quality of the ASR component on the MT performance, by comparing a series of WERs with the corresponding automatic translation scores. By porting an STE-ASR framework to the task at hand, we show how the word errors for transcribing English and Spanish speeches can be lowered by 3.0% and 4.8% relative, respectively.