Abstract Meaning Representation for Paraphrase Detection

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 F 1 measure.

A Task in a Suit and a Tie: Paraphrase Generation with Semantic Augmentation

“…Our results also indicate considerable headroom for granting semantics a greater role throughout-in representation, architecture design and task evaluation. This emphasis is in line with other recent and related work, including entailment in paraphrasing (Pavlick et al 2015), paraphrase identification with Abstract Meaning Representations (Issa et al 2018) and using semantic role labeling in machine translation (Marcheggiani, Bastings, and Titov 2018). It should be noted that this work is only a first step toward paraphrasing at the quality exemplified in Table 1.…”

A hybrid approach for paraphrase identification based on knowledge-enriched semantic heuristics

“…First, the application of strategies entirely or substantially based on corpus statistics provided some success in addressing the paraphrase identification problem (Blacoe and Lapata 2012;Ji and Eisenstein 2013;Issa et al 2018). Ji and Eisenstein (2013) used a simple distributional similarity model by designing a discriminative termweighting metric called TF-KLD.…”

“…Alternatively, Wan et al (2006) exploited a machine learning approach using lexical and syntactic dependency-based features whereas other researchers including (Madnani et al 2012;Finch et al 2005) investigated the feasibility of WordNet-based machine translation approaches for paraphrase detection. With varying levels of performance on the MSRPC dataset, this category (Corpus-based) contains some of the bestperforming methods, notably the works of Ji and Eisenstein (2013) and Issa et al (2018), which achieved accuracy figures of 80.4% and 86.6% 1 respectively.…”

SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA

“…Examples include Abstract Meaning Representation (AMR; Banarescu et al, 2013), Broad-coverage Semantic Dependencies (SDP; Oepen et al, 2016), Universal Decompositional Semantics (UDS; White et al, 2016), Parallel Meaning Bank (Abzianidze et al, 2017), and Universal Conceptual Cognitive Annotation (UCCA; Abend and Rappoport, 2013). These advances in semantic representation, along with corresponding advances in semantic parsing, can potentially benefit essentially all text understanding tasks, and have already demonstrated applicability to a variety of tasks, including summarization (Liu et al, 2015;Dohare and Karnick, 2017), paraphrase detection (Issa et al, 2018), and semantic evaluation (using UCCA; see below). In this shared task, we focus on UCCA parsing in multiple languages.…”