An Empirical Comparison of Parsers in Constraining Reordering for E-J Patent Machine Translation

Abstract

Machine translation of patent documents is very important from a practical point of view. One of the key technologies for improving machine translation quality is the utilization of syntax. It is difficult to select the appropriate parser for English to Japanese patent machine translation because the effects of each parser on patent translation are not clear. This paper provides an empirical comparative evaluation of several state-of-the-art parsers for English, focusing on the effects on patent machine translation from English to Japanese. We add syntax to a method that constrains the reordering of noun phrases for phrase-based statistical machine translation. There are two methods for obtaining the noun phrases from input sentences: 1) an input sentence is directly parsed by a parser and 2) noun phrases from an input sentence are determined by a method using the parsing results of the context document that contains the input sentence. We measured how much each parser contributed to improving the translation quality for each of the two methods and how much a combination of parsers contributed to improving the translation quality for the second method. We conducted experiments using the NTCIR-8 patent translation task dataset. Most of the parsers improved translation quality. Combinations of parsers using the method based on context documents achieved the best translation quality.