SSMT:A Machine Translation Evaluation View To Paragraph-to-Sentence Semantic Similarity

Pingping Huang, Baobao Chang
2014 Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)  
This paper presents the system SSMT measuring the semantic similarity between a paragraph and a sentence submitted to the SemEval 2014 task3: Cross-level Semantic Similarity. The special difficulty of this task is the length disparity between the two semantic comparison texts. We adapt several machine translation evaluation metrics for features to cope with this difficulty, then train a regression model for the semantic similarity prediction. This system is straightforward in intuition and easy
more » ... intuition and easy in implementation. Our best run gets 0.808 in Pearson correlation. METEORderived features are the most effective ones in our experiment.
doi:10.3115/v1/s14-2102 dblp:conf/semeval/HuangC14 fatcat:usbu5ennofh3fjtkzyw673qrjy