Using Parallel Corpora to Automatically Generate Training Data for Chinese Segmenters in NTCIR PatentMT Tasks

Chinese texts do not contain spaces as word separators like Eng-lish and many alphabetic languages. To use Moses to train transla-tion models, we must segment Chinese texts into sequences of Chinese words. Increasingly more software tools for Chinese segmentation are populated on the Internet in recent years. How-ever, some of these tools were trained with general texts, so might not handle domain-specific terms in patent documents very well. Some machine-learning based tools require us to provide seg-mented Chinese to train segmentation models. In both cases, providing segmented Chinese texts to refine a pre-trained model or to create a new model for segmentation is an important basis for successful Chinese-English machine translation systems. Ide-ally, high-quality segmented texts should be created and verified by domain experts, but doing so would be quite costly. We ex-plored an approach to algorithmically generate segmented texts with parallel texts and lexical resources. Our scores in NTCIR-10 PatentMT indeed improved from our scores in NTCIR-9 PatentMT with the new approach. Categories and Subject Descriptor