Dominikus Wetzel

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Recent work has shown that the integration of visual information into text-based models can substantially improve model predictions, but so far only visual information extracted from static images has been used. In this paper, we consider the problem of grounding sentences describing actions in visual information extracted from videos. We present a general(More)
This paper presents an approach to improving performance of statistical machine translation by automatically creating new training data for difficult to translate phenomena. In particular this contribution is targeted towards tackling the poor performance of a state-of-the-art system on negated sentences. The corpus expansion is achieved by high quality(More)
We present a maximum entropy classifier for cross-lingual pronoun prediction. The features are based on local source-and target-side contexts and antecedent information obtained by a co-reference resolution system. With only a small set of feature types our best performing system achieves an accuracy of 72.31%. According to the shared task's official(More)
We present our submission to the cross-lingual pronoun prediction (CLPP) shared task for English-German and English-French at the First Conference on Machine Translation (WMT16). We trained a Maximum Entropy (MaxEnt) classifier based on features from Wetzel et al. (2015), that we adapted to the new task and applied to a new language pair. Additional(More)
This work presents an extension to phrase-based statistical machine translation models which incorporates linguistic knowledge , namely part-of-speech information. Scores are added to the standard phrase table which represent how the phrases correspond to their translations on the part-of-speech level. We suggest two different kinds of scores. They are(More)
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