AMR Parsing with an Incremental Joint Model

Abstract

To alleviate the error propagation in the traditional pipelined models for Abstract Meaning Representation (AMR) parsing, we formulate AMR parsing as a joint task that performs the two subtasks: concept identification and relation identification simultaneously. To this end, we first develop a novel componentwise beam search algorithm for relation identification in an incremental fashion, and then incorporate the decoder into a unified framework based on multiple-beam search, which allows for the bi-directional information flow between the two subtasks in a single incremental model. Experiments on the public datasets demonstrate that our joint model significantly outperforms the previous pipelined counterparts, and also achieves better or comparable performance than other approaches to AMR parsing, without utilizing external semantic resources.

Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

“…AMR parsing thus requires solving several natural language processing tasks; named entity recognition, word sense disambiguation and joint syntactic and semantic role labeling. AMR parsing has acquired a lot of attention (Wang et al, 2015a;Zhou et al, 2016;Wang et al, 2015b;Goodman et al, 2016;Guo and Lu, 2018;Lyu and Titov, 2018;Vilares and Gómez-Rodríguez, 2018;Zhang et al, 2019) in recent years.…”

A Transition-Based Directed Acyclic Graph Parser for UCCA

“…Transition-based techniques are a natural starting point for UCCA parsing, given the conceptual similarity of UCCA's distinctions, centered around predicate-argument structures, to distinctions expressed by dependency schemes, and the achievements of transition-based methods in dependency parsing (Dyer et al, 2015;Andor et al, 2016;Kiperwasser and Goldberg, 2016). We are further motivated by the strength of transition-based methods in related tasks, including dependency graph parsing (Sagae and Tsujii, 2008;Ribeyre et al, 2014;Tokgöz and Eryigit, 2015), constituency parsing (Sagae and Lavie, 2005;Zhang and Clark, 2009;Zhu et al, 2013;Maier, 2015;Maier and Lichte, 2016), AMR parsing (Wang et al, 2015a(Wang et al, ,b, 2016Misra and Artzi, 2016;Goodman et al, 2016;Zhou et al, 2016;Damonte et al, 2017) and CCG parsing (Zhang and Clark, 2011;Ambati et al, 2015Ambati et al, , 2016.…”

Getting the Most out of AMR Parsing

“…Pust et al (2015) formulates AMR parsing as a machine translation problem in which the sentence is the source language input and the AMR is the target language output. AMR parsing systems that focus on modeling the graph aspect of the AMR includes JAMR (Flanigan et al, 2014(Flanigan et al, , 2016aZhou et al, 2016), which treats AMR parsing as a procedure for searching for the Maximum Spanning Connected Subgraphs (MSCGs) from an edge-labeled, directed graph of all possible relations. Parsers based on Hyperedge Replacement Grammars (HRG) (Chiang et al, 2013;Björklund et al, 2016;Groschwitz et al, 2015) put more emphasis on modeling the formal properties of the AMR graph.…”

“…We include the previous best results on this dataset. The parser proposed in (Zhou et al, 2016) jointly learns the concept and relation through an incremental joint model. We also include the AMR parser by (Pust et al, 2015) that models AMR parsing as a machine translation task and incorporates various external resources.…”

“…A number of AMR parsers have been developed in recent years (Flanigan et al, 2014;Wang et al, 2015b;Artzi et al, 2015;Pust et al, 2015;Peng et al, 2015;Zhou et al, 2016;Goodman et al, 2016a), and the initial benefit of AMR parsing has been demonstrated in various downstream applications such as Information Extraction (Pan et al, 2015;Huang et al, 2016), Machine Comprehension (Sachan and Xing, 2016), and Language Generation (Flanigan et al, 2016b;Butler, 2016). However, AMR parsing parsing accuracy is still in the high 60%, as measured by the SMatch score , and a significant improvement is needed in order for it to positively impact a larger number of applications.…”