Felix Stahlberg

Learn More
We investigate the use of hierarchical phrase-based SMT lattices in end-to-end neural machine translation (NMT). Weight pushing transforms the Hiero scores for complete translation hypotheses, with the full translation grammar score and full ngram language model score, into posteriors compatible with NMT predictive probabilities. With a slightly modified(More)
We present a novel scheme to combine neural machine translation (NMT) with traditional statistical machine translation (SMT). Our approach borrows ideas from linearised lattice minimum Bayes-risk decoding for SMT. The NMT score is combined with the Bayes-risk of the translation according the SMT lattice. This makes our approach much more flexible than(More)
We present our new alignment model Model 3P for cross-lingual word-to-phoneme alignment, and show that unsupervised learning of word segmentation is more accurate when information of another language is used. Word segmentation with cross-lingual information is highly relevant to bootstrap pronunciation dictionaries from audio data for Automatic Speech(More)
With the help of written translations in a source language, we cross-lingually segment phoneme sequences in a target language into word units using our new alignment model Model 3P [17]. From this, we deduce phonetic transcriptions of target language words, introduce the vocabulary in terms of word IDs, and extract a pronunciation dictionary. Our approach(More)
In this paper we tackle the task of bootstrapping an Automatic Speech Recognition system without an a priori given language model, a pronunciation dictionary, or transcribed speech data for the target language Slovene – only untranscribed speech and translations to other resource-rich source languages of what was said are available. Therefore, our approach(More)
Since Arabic script has a strong baseline, many state-of-the-art recognition systems for handwritten Arabic make use of baseline-dependent features. For printed Arabic, the baseline can be detected reliably by finding the maximum in the horizontal projection profile or the Hough transformed image. However, the performance of these methods drops(More)
This paper presents the University of Cambridge submission to WMT16. Motivated by the complementary nature of syntactical machine translation and neural machine translation (NMT), we exploit the synergies of Hiero and NMT in different combination schemes. Starting out with a simple neural lattice rescoring approach, we show that the Hiero lattices are often(More)
We compare several language models for the word-ordering task and propose a new bagto-sequence neural model based on attentionbased sequence-to-sequence models. We evaluate the model on a large German WMT data set where it significantly outperforms existing models. We also describe a novel search strategy for LM-based word ordering and report results on the(More)