This paper discusses neural machine translation (NMT), a new paradigm in the MT field, comparing the quality of NMT systems with statistical MT by describing three studies using automatic and human evaluation methods. Automatic evaluation results presented for NMT are very promising, however human evaluations show mixed results. We report increases in fluency but inconsistent results for adequacy and post-editing effort. NMT undoubtedly represents a step forward for the MT field, but one that the community should be careful not to oversell.
We use existing tools to automatically build two parallel treebanks from existing parallel corpora. We then show that combining the data extracted from both the treebanks and the corpora into a single translation model can improve the translation quality in a baseline phrasebased statistical machine translation system.
Given much recent discussion and the shift in focus of the field, it is becoming apparent that the incorporation of syntax is the way forward for improvements to the current state-of-the-art in machine translation (MT). Parallel treebanks are a relatively recent innovation and appear to be ideal candidates for MT training material. However, until recently there has been no other means to build them than by hand. In this paper, we describe how we make use of new tools to automatically build a large parallel treebank and extract a set of linguistically-motivated phrase pairs from it. We show that adding these phrase pairs to the translation model of a baseline phrase-based statistical MT (PB-SMT) system leads to significant improvements in translation quality. Following this, we describe experiments in which we exploit the information encoded in the parallel treebank in other areas of the PB-SMT framework, while investigating the conditions under which the incorporation of parallel treebank data performs optimally. Finally, we discuss the possibility of exploiting automatically-generated parallel treebanks further in syntax-aware paradigms of MT.
Abstract. We describe OpenMaTrEx, a free/open-source examplebased machine translation (EBMT) system based on the marker hypothesis, comprising a marker-driven chunker, a collection of chunk aligners, and two engines: one based on a simple proof-of-concept monotone EBMT recombinator and a Moses-based statistical decoder. OpenMaTrEx is a free/open-source release of the basic components of MaTrEx, the Dublin City University machine translation system.
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