This work presents two different translation models using recurrent neural networks. The first one is a word-based approach using word alignments. Second, we present phrase-based translation models that are more consistent with phrasebased decoding. Moreover, we introduce bidirectional recurrent neural models to the problem of machine translation, allowing us to use the full source sentence in our models, which is also of theoretical interest. We demonstrate that our translation models are capable of improving strong baselines already including recurrent neural language models on three tasks: IWSLT 2013 German→English, BOLT Arabic→English and Chinese→English. We obtain gains up to 1.6% BLEU and 1.7% TER by rescoring 1000-best lists.

Word alignment was once a core unsupervised learning task in natural language processing because of its essential role in training statistical machine translation (MT) models. Although unnecessary for training neural MT models, word alignment still plays an important role in interactive applications of neural machine translation, such as annotation transfer and lexicon injection. While statistical MT methods have been replaced by neural approaches with superior performance, the twenty-year-old GIZA++ toolkit remains a key component of state-of-the-art word alignment systems. Prior work on neural word alignment has only been able to outperform GIZA++ by using its output during training. We present the first end-to-end neural word alignment method that consistently outperforms GIZA++ on three data sets. Our approach repurposes a Transformer model trained for supervised translation to also serve as an unsupervised word alignment model in a manner that is tightly integrated and does not affect translation quality.

We apply phrase-based and neural models to a core task in interactive machine translation: suggesting how to complete a partial translation. For the phrase-based system, we demonstrate improvements in suggestion quality using novel objective functions, learning techniques, and inference algorithms tailored to this task. Our contributions include new tunable metrics, an improved beam search strategy, an n-best extraction method that increases suggestion diversity, and a tuning procedure for a hierarchical joint model of alignment and translation. The combination of these techniques improves next-word suggestion accuracy dramatically from 28.5% to 41.2% in a large-scale English-German experiment. Our recurrent neural translation system increases accuracy yet further to 53.0%, but inference is two orders of magnitude slower. Manual error analysis shows the strengths and weaknesses of both approaches.

We propose and compare methods for gradientbased domain adaptation of self-attentive neural machine translation models. We demonstrate that a large proportion of model parameters can be frozen during adaptation with minimal or no reduction in translation quality by encouraging structured sparsity in the set of offset tensors during learning via group lasso regularization. We evaluate this technique for both batch and incremental adaptation across multiple data sets and language pairs. Our system architecture-combining a state-of-the-art self-attentive model with compact domain adaptation-provides high quality personalized machine translation that is both space and time efficient.

This paper describes one of the collaborative efforts within EU-BRIDGE to further advance the state of the art in machine translation between two European language pairs, German→English and English→German. Three research institutes involved in the EU-BRIDGE project combined their individual machine translation systems and participated with a joint setup in the shared translation task of the evaluation campaign at the ACL 2014 Eighth Workshop on Statistical Machine Translation (WMT 2014).We combined up to nine different machine translation engines via system combination. RWTH Aachen University, the University of Edinburgh, and Karlsruhe Institute of Technology developed several individual systems which serve as system combination input. We devoted special attention to building syntax-based systems and combining them with the phrasebased ones. The joint setups yield empirical gains of up to 1.6 points in BLEU and 1.0 points in TER on the WMT newstest2013 test set compared to the best single systems.