AMR dependency parsing with a typed semantic algebra

Abstract

We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system. We present two approximative decoding algorithms, which achieve state-of-the-art accuracy and outperform strong baselines. Related WorkRecently, AMR parsing has generated considerable research activity, due to the availability of large-

AMR Parsing as Sequence-to-Graph Transduction

“…Corpus Parser F1(%) AMR 2.0 Buys and Blunsom (2017) 61.9 van Noord and Bos (2017b) 71.0 * Groschwitz et al (2018) 71.0±0.5 Lyu and Titov (2018) 74.4±0.2 Naseem et al (2019) 75.5…”

“…(2) the lack of gold alignments between nodes (concepts) in the graph and words in the text which limits attempts to rely on explicit alignments to generate training data (Flanigan et al, 2014;Wang et al, 2015;Damonte et al, 2017;Foland and Martin, 2017;Peng et al, 2017b;Groschwitz et al, 2018;Guo and Lu, 2018); and (3) relatively limited amounts of labeled data (Konstas et al, 2017).…”

Core Semantic First: A Top-down Approach for AMR Parsing

“…2 Since the size of AMR graph is approximately linear in the length of sentence. 2015; Foland and Martin, 2017;Lyu and Titov, 2018;Groschwitz et al, 2018;Guo and Lu, 2018). The hand-crafted rules for re-categorization are often non-trivial, requiring exhaustive screening and expert-level manual efforts.…”

SemBleu: A Robust Metric for AMR Parsing Evaluation

“…Table 4 shows the SEMBLEU and SMATCH scores several recent models. In particular, we asked for the outputs of Lyu (Lyu and Titov, 2018), Gros (Groschwitz et al, 2018), van Nood (van Noord and Bos, 2017) and Guo (Guo and Lu, 2018) to evaluate on our SEMBLEU. For CAMR and JAMR, we obtain their outputs by running the released systems.…”

“…Despite the large amount of work on AMR parsing (Flanigan et al, 2014;Artzi et al, 2015;Pust et al, 2015;Peng et al, 2015;Buys and Blunsom, 2017;Konstas et al, 2017;Wang and Xue, 2017;Ballesteros and Al-Onaizan, 2017;Lyu and Titov, 2018;Peng et al, 2018;Groschwitz et al, 2018;Guo and Lu, 2018), little attention has been paid to evaluating the parsing results, leaving SMATCH Figure 2: Average, minimal and maximal SMATCH scores over 100 runs on 100 sentences. The running time increases from 6.6 seconds (r=1) to 21.0 (r = 4).…”