Parallel sentences are crucial for statistical machine translation (SMT). However, they are quite scarce for most language pairs, such as Chinese-Japanese. Many studies have been conducted on extracting parallel sentences from noisy parallel or comparable corpora. We extract Chinese-Japanese parallel sentences from quasi-comparable corpora, which are available in far larger quantities. The task is significantly more difficult than the extraction from noisy parallel or comparable corpora. We extend a previous study that treats parallel sentence identification as a binary classification problem. Previous method of classifier training by the Cartesian product is not practical, because it differs from the real process of parallel sentence extraction. We propose a novel classifier training method that simulates the real sentence extraction process. Furthermore, we use linguistic knowledge of Chinese character features. Experimental results on quasi-comparable corpora indicate that our proposed approach performs significantly better than the previous study.
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