This paper describes the statistical machine translation system developed at RWTH Aachen University for the English→German and German→English translation tasks of the EMNLP 2017 Second Conference on Machine Translation (WMT 2017). We use ensembles of attention-based neural machine translation system for both directions. We use the provided parallel and synthetic data to train the models. In add...

This paper describes the statistical machine translation system developed at RWTH Aachen University for the German→English translation task of the EMNLP 2015 Tenth Workshop on Statistical Machine Translation (WMT 2015). A phrase-based machine translation system was applied and augmented with hierarchical phrase reordering and word class language models. Further, we ran discriminative maximum ex...

This paper presents the overview of statistical machine translation systems that BJTU-NLP developed for the NTCIR-9 Patent Machine Translation Task (NTCIR-9 PatentMT). We compared the performance between phrase-based translation model and factored translation model in our Patent SMT of Chinese to English and English to Japanese. Factored translation model was proposed as an extended phrase-base...

Machine Translation (MT) systems have historically relied upon explicit grammars in order to analyze the source text and reproduce it in the target language. In this paper, we argue for a style of MT in which the focus of processing is at the level of the lexicon, rather than the grammar. This approach to translation allows an analyzer to map source sentences into an interlingual form, which th...

To the Western mind the Chinese language, particularly the written language, has long been a source of wonder. Because the Westerner has known an alphabet from childhood he can scarcely believe that the strange ideographic characters of Chinese can convey the whole gamut of human thought, from philosophy to platitudes. Even without the added complication of ideographic writing, the Chinese lang...

In recent years there has been an enormous boom in MT research. There has been not only an increase in the number of research groups in the field and in the amount of funding, but there is now also optimism for the future of the field and for achieving even better quality. The major reason for this change has been a paradigm shift away from linguistic/rule-based methods towards empirical/data-d...

Training efficiency is one of the main problems for Neural Machine Translation (NMT). Deep networks, very large data and many training iterations are necessary to achieve state-of-the-art performance for NMT. This results in very high computation cost and slow down research and industrialization. In this paper, we first investigate the instability by randomizations for NMT training, and further...

The vanilla attention-based neural machine translation has achieved promising performance because of its capability in leveraging varying-length source annotations. However, this model still suffers from failures in long sentence translation, for its incapability in capturing long-term dependencies. In this paper, we propose a novel recurrent neural machine translation (RNMT), which not only pr...

This talk discusses some o f the challenges posed for theoretical linguistics by transla­ tional problems. It briefly discusses different aspects o f the concept o f translational equivalence and the degree to which this relation may be captured by machine translation (MT) systems. It is argued that the formal requirements and multilingual perspective of MT provide new criteria o f adequacy for...