Abstract Meaning Representation Parsing using LSTM Recurrent Neural Networks

Abstract

We present a system which parses sentences into Abstract Meaning Representations, improving state-of-the-art results for this task by more than 5%. AMR graphs represent semantic content using linguistic properties such as semantic roles, coreference, negation, and more. The AMR parser does not rely on a syntactic preparse, or heavily engineered features, and uses five recurrent neural networks as the key architectural components for inferring AMR graphs.

AMR dependency parsing with a typed semantic algebra

“…Our results are averages over 4 runs with 95% confidence intervals (JAMR-style baselines are single runs). On the 2015 dataset, our best models (local + projective, K&G + fixed-tree) outperform all previous work, with the exception of the Foland and Martin (2017) model; on the 2017 set we match state of the art results (though note that van Noord and Bos (2017b) use 100k additional sentences of silver data). The fixed-tree decoder seems to work well with either edge model, but performance of the projective decoder drops with the K&G edge scores.…”

“…Table 2 analyzes the performance of our two best systems (PD = projective, FTD = fixed-tree) in more detail, using the categories of Damonte et al (2017), and compares them to Wang's, Flanigan's, and Damonte's AMR parsers on the 2015 set and , and van Noord and Bos (2017b) for the 2017 dataset. (Foland and Martin (2017) did not publish such results.) The good scores we achieve on reentrancy identification, despite removing a large amount of reentrant edges from the training data, indicates that our elementary as-graphs successfully encode phenomena such as control and coordination.…”

“…We investigate the impact of this choice by implementing a strong JAMR-style baseline. We adapt the AMR-todependency conversion of Section 4.2 by removing all unlabeled nodes with source names from the (Wang et al, 2015) 66.5 -JAMR (Flanigan et al, 2016) 67 - Damonte et al (2017) 64 van Noord and Bos (2017b) 68.5 71.0 Foland and Martin (2017) 70.7 -Buys and Blunsom (2017) -61.9 Fig. 1 now only consists of a single "want" node.…”

AMR Parsing as Graph Prediction with Latent Alignment

“…In order to deal with these cases, we re-categorized AMR concepts. Similar recategorization strategies have been used in previous work (Foland and Martin, 2017;Peng 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.…”