HLTC-HKUST: A Neural Network Paraphrase Classifier using Translation Metrics, Semantic Roles and Lexical Similarity Features

Abstract

This paper describes the system developed by our team (HLTC-HKUST) for task 1 of SemEval 2015 workshop about paraphrase classification and semantic similarity in Twitter. We trained a neural network classifier over a range of features that includes translation metrics, lexical and syntactic similarity score and semantic features based on semantic roles. The neural network was trained taking into consideration in the objective function the six different similarity levels provided in the corpus, in order to give as output a more fine-grained estimation of the similarity level of the two sentences, as required by subtask 2. With an F-score of 0.651 in the binary paraphrase classification subtask 1, and a Pearson coefficient of 0.697 for the sentence similarity subtask 2, we achieved respectively the 6th place and the 3rd place, above the average of what obtained by the other contestants.