Matching Graph, a Method for Extracting Parallel Information from Comparable Corpora

Abstract

Comparable corpora are valuable alternatives for the expensive parallel corpora. They comprise informative parallel fragments that are useful resources for different natural language processing tasks. In this work, a generative model is proposed for efficient extraction of parallel fragments from a pair of comparable documents. The core of the proposed model is a graph called the Matching Graph. The ability of the Matching Graph to be trained on a small initial seed makes it a proper model for language pairs suffering from the scarce resource problem. Experiments show that the Matching Graph performs significantly better than other recently published models. According to the experiments on English-Persian and Arabic-Persian language pairs, the extracted parallel fragments can be used instead of parallel data for training statistical machine translation systems. Results reveal that the extracted fragments in the best case are able to retrieve about 90% of the information of a statistical machine translation system that is trained on a parallel corpus. Moreover, it is shown that using the extracted fragments as additional information for training statistical machine translation systems leads to an improvement of about 2% for English-Persian and about 1% for Arabic-Persian translation on BLEU score.