Inflating a Small Parallel Corpus into a Large Quasi-parallel Corpus Using Monolingual Data for Chinese-Japanese Machine Translation

Abstract

Increasing the size of parallel corpora for less-resourced language pairs is essential for machine translation (MT). To address the shortage of parallel corpora between Chinese and Japanese, we propose a method to construct a quasi-parallel corpus by inflating a small amount of Chinese-Japanese corpus, so as to improve statistical machine translation (SMT) quality. We generate new sentences using analogical associations based on large amounts of monolingual data and a small amount of parallel data. We filter over-generated sentences using two filtering methods: one based on BLEU and the second one based on N-sequences. We add the obtained aligned quasi-parallel corpus to a small parallel Chinese-Japanese corpus and perform SMT experiments. We obtain significant improvements over a baseline system.

Case-Based Translation: First Steps from a Knowledge-Light Approach Based on Analogy to a Knowledge-Intensive One

“…This technique has never been applied to example-based machine translation, but it has been used for statistical machine translation to create new pairs of aligned sentences (in CBR terms, source problems and their solutions) so as to augment the training data (the case base in CBR terms) [41]. Analogical clusters identify well attested transformations, which should thus be reliable.…”