Building Subject-aligned Comparable Corpora and Mining it for Truly Parallel Sentence Pairs
This work addresses the practical problem of limited parallel data for machine translation and cross-lingual retrieval, offering an incremental improvement by mining more useful resources from abundant non-parallel multilingual sources.
The researchers tackled the scarcity of parallel sentences for cross-lingual applications by developing a method to build subject-aligned comparable corpora from Wikipedia and extract truly parallel sentences from noisy data, resulting in a specialized tool and improved machine translation system.
Parallel sentences are a relatively scarce but extremely useful resource for many applications including cross-lingual retrieval and statistical machine translation. This research explores our methodology for mining such data from previously obtained comparable corpora. The task is highly practical since non-parallel multilingual data exist in far greater quantities than parallel corpora, but parallel sentences are a much more useful resource. Here we propose a web crawling method for building subject-aligned comparable corpora from Wikipedia articles. We also introduce a method for extracting truly parallel sentences that are filtered out from noisy or just comparable sentence pairs. We describe our implementation of a specialized tool for this task as well as training and adaption of a machine translation system that supplies our filter with additional information about the similarity of comparable sentence pairs.