English-French Verb Phrase Alignment in Europarl for Tense Translation Modeling
Sharid Loáiciga, Thomas Meyer, Andréi Popescu-Belis · 2014
This paper presents a method for verb phrase (VP) alignment in an English/French parallel corpus and its use for improving statistical machine translation (SMT) of verb tenses.The method starts from automatic word alignment performed with GIZA++, and relies on a POS tagger and a parser, in combination with several heuristics, in order to identify non-contiguous components of VPs, and to label the aligned VPs with their tense and voice on each side.This procedure is applied to the Europarl corpus, leading to the creation of a smaller, high-precision parallel corpus with about 320 000 pairs of finite VPs, which is made publicly available.This resource is used to train a tense predictor for translation from English into French, based on a large number of surface features.Three MT systems are compared: (1) a baseline phrase-based SMT; (2) a tense-aware SMT system using the above predictions within a factored translation model; and (3) a system using oracle predictions from the aligned VPs.For several tenses, such as the French imparfait, the tense-aware SMT system improves significantly over the baseline and is closer to the oracle system.