Robust Subgraph Generation Improves Abstract Meaning Representation Parsing

Abstract

The Abstract Meaning Representation (AMR) is a representation for opendomain rich semantics, with potential use in fields like event extraction and machine translation. Node generation, typically done using a simple dictionary lookup, is currently an important limiting factor in AMR parsing. We propose a small set of actions that derive AMR subgraphs by transformations on spans of text, which allows for more robust learning of this stage. Our set of construction actions generalize better than the previous approach, and can be learned with a simple classifier. We improve on the previous state-of-the-art result for AMR parsing, boosting end-to-end performance by 3 F 1 on both the LDC2013E117 and LDC2014T12 datasets.

A Transition-Based Directed Acyclic Graph Parser for UCCA

“…While sharing much of this work's motivation, not anchoring the representation in the text complicates the parsing task, as it requires the alignment to be automatically (and imprecisely) detected. Indeed, despite considerable technical effort Pourdamghani et al, 2014;Werling et al, 2015), concept identification is only about 80%-90% accurate. Furthermore, anchoring allows breaking down sentences into semantically meaningful sub-spans, which is useful for many applications (Fernández-González and Martins, 2015;Birch et al, 2016).…”

CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks

“…(Wang et al, 2015;Vanderwende et al, 2015;Peng et al, 2015;Pust et al, 2015;Artzi et al, 2015;Flanigan et al, 2014;Werling et al, 2015). In contrast, we follow the spirit of minimal feature extraction using pre-trained word embeddings, as in (Collobert et al, 2011) and a recurrent network architecture similar to that described in (Zhou and Xu, 2015).…”

Unsupervised AMR-Dependency Parse Alignment

“…When separating the alignments into roles (edge labels) and non-roles (concepts), F 1 scores are 49.3% and 89.8%, respectively. In Werling's AMR parser (Werling et al, 2015), they conceive of the alignment task as a linear programming relaxation of a boolean problem. The objective function is to maximize the sum of action reliability.…”