AAAI Publications, Twenty-Seventh AAAI Conference on Artificial Intelligence

Font Size: 
A Topic-Based Coherence Model for Statistical Machine Translation
Deyi Xiong, Min Zhang

Last modified: 2013-06-30

Abstract


Coherence that ties sentences of a text into a meaningfully connected structure is of great importance to text generation and translation. In this paper, we propose a topic-based coherence model to produce coherence for document translation, in terms of the continuity of sentence topics in a text. We automatically extract a coherence chain for each source text to be translated. Based on the extracted source coherence chain, we adopt a maximum entropy classifier to predict the target coherence chain that defines a linear topic structure for the target document. The proposed topic-based coherence model then uses the predicted target coherence chain to help decoder select coherent word/phrase translations. Our experiments show that incorporating the topic-based coherence model into machine translation achieves substantial improvement over both the baseline and previous methods that integrate document topics rather than coherence chains into machine translation.

Keywords


Statistical Machine Translation; Coherence; Topic

Full Text: PDF