Automatically generated parallel treebanks and their exploitability in machine translation

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.