Abstract Meaning Representation for Paraphrase Detection
Fuad Issa, Marco Damonte, Shay B. Cohen, Xiaohui Yan, Yi Chang
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Abstract Meaning Representation (AMR) parsing aims at abstracting away from the syntactic realization of a sentence, and denote only its meaning in a canonical form. As such, it is ideal for paraphrase detection, a problem in which one is required to specify whether two sentences have the same meaning. We show that na\" ve use of AMR in paraphrase detection is not necessarily useful, and turn to describe a technique based on latent semantic analysis in combination with AMR parsing that significantly advances state-of-the-art results in paraphrase detection for the Microsoft Research Paraphrase Corpus. Our best results in the transductive setting are 86.6\% for accuracy and 90.0\% for F_1 measure.