We propose a conversion of bilingual sentence pairs and the corresponding word alignments into novel linear sequences.These are joint translation and reordering (JTR) uniquely defined sequences, combining interdepending lexical and alignment dependencies on the word level into a single framework.They are constructed in a simple manner while capturing multiple alignments and empty words.JTR sequences can be used to train a variety of models.We investigate the performances of ngram models with modified Kneser-Ney smoothing, feed-forward and recurrent neural network architectures when estimated on JTR sequences, and compare them to the operation sequence model (Durrani et al., 2013b).Evaluations on the IWSLT German→English, WMT German→English and BOLT Chinese→English tasks show that JTR models improve state-of-the-art phrasebased systems by up to 2.2 BLEU.

Conditional Random Fields (CRFs) are a state-of-the-art approach to natural language processing tasks like grapheme-to phoneme (g2p) conversion which is used to produce pronunciations or pronunciation variants for almost all ASR pronunciation lexica. One drawback of CRFs is that for training, an alignment is needed between graphemes and phonemes, usually even 1-to-l. The quality of the g2p result heavily depends on this alignment. Since these alignments are usually not annotated within the corpora, external models have to be used to produce such an alignment in a preprocessing step. In this work, we propose two approaches to integrate the alignment generation directly and efficiently into the CRF training process. Whereas the first approach relies on linear segmentation as starting point, the second approach considers all possible alignments given certain constraints. Both methods have been evaluated on two English g2p tasks, namely NETtalk and Celex, on which state-of-the-art results have been reported in the literature. The proposed approaches lead to results comparable to the state-of-the art.