Projects per year
Abstract Meaning Representation (AMR) parsing aims at abstracting away from the syntactic realization of a sentence, and denoting 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 naïve 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 F1 measure.
|Title of host publication||The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies|
|Place of Publication||New Orleans, Louisiana, USA|
|Publisher||Association for Computational Linguistics|
|Number of pages||11|
|Publication status||E-pub ahead of print - 6 Jun 2018|
|Event||16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Hyatt Regency New Orleans Hotel, New Orleans, United States|
Duration: 1 Jun 2018 → 6 Jun 2018
|Conference||16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies|
|Abbreviated title||NAACL HLT 2018|
|Period||1/06/18 → 6/06/18|
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- 1 Finished
1/02/16 → 31/01/19