A Source Dependency Model for Statistical Machine Translation
نویسندگان
چکیده
In the formally syntax-based MT, a hierarchical tree generated by synchronous CFG rules associates the source sentence with the target sentence. In this paper, we propose a source dependency model to estimate the probability of the hierarchical tree generated in decoding. We develop this source dependency model from word-aligned corpus, without using any linguistically motivated parsing. Our experimental results show that integrating the source dependency model into the formally syntax-based machine translation significantly improves the performance on Chinese-to-English translation tasks.
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