Simpler Is Better: Re-evaluation of Default Word Alignment Models in Statistical MT
Mark Fishel · Institutional Repositories DataBase (IRDB) · 2010
Abstract. Although several recent studies have shown that alignment quality is a poor indicator of the resulting translation quality, the word alignment models currently considered to be default (the so-called IBM models and HMM-based alignment) have been evaluated using the alignment error rate. We argue that from a machine translation perspective it makes sense to use simpler alignment models. Here we show that not only do the sequential models result in the same or better translation quality, but even from the set of sequential alignment models simpler ones can match the performance of the HMM-based model, whereas using computationally less expensive and faster algorithms to train and align new sentence pairs. Empirical evaluation is performed on a phrase-based and a parsing-based translation system.