Semantic Dependency Parsing with Edge GNN s (2022)

Abstract: Second-order neural parsers have obtained high accuracy in semantic dependency parsing. Inspired by the factor graph representation of second-order parsing, we propose edge graph neural networks (E-GNNs). In an E-GNN, each node corresponds to a dependency edge, and the neighbors are defined in terms of sibling, co-parent, and grandparent relationships. We conduct experiments on SemEval 2015 Task 18 English datasets, showing the superior performance of E-GNNs.

## introduction traditional syntactic dependency parsing aims to produce a tree structure for a given sentence, which has been well-studied. however, tree-structured representation is ill-suited for producing meaning representation, which motivates the proposal of semantic dependency parsing (sdp) @xcite . sdp aims to produce a directed acyclic graph (dag) instead of a tree to enable representing more complex semantic relationships. graph-based methods have obtained high accuracy in sdp @xcite @xcite . notably, @xcite propose a second-order neural crf parser and show superior performance compared to the first-order biaffine parser @xcite . to optimize the intractable crf objective, they leverage approximate inference algorithms such as loopy belief propagation (lbp), unrolling several inference steps as recurrent neural networks @xcite for end-to-end approximationaware training @xcite . however, their model suffers from the following two problems: (i) second-order dependency parsing can be formulated in terms of factor graphs (smith and eisner, 2008). however, the corresponding factor graph for second-order parsing is highly loopy (fig. 1 ). it is known that on loopy graphs, lbp can easily get stuck on bad local optima, leading to sub-optimal results and thus undermining the parsing performance. (ii) first-order and second-order scores are produced based solely on contextualized word representations, which is deemed to be sub-optimal @xcite . in this work, we propose edge gnns (e-gnns) to address the aforementioned limitation of @xcite . inspired by factor graph representations of second-order sdp where each variable node corresponds to a dependency edge (fig. 1 ), we take edges as gnn nodes and define neighbors in terms of sibling, co-parent, and grandparent relationships. the benefit is shown as follows. we conduct experiments on semeval 2015 task 18 english datasets of sdp, showing superior performance compared with @xcite . ## related work dependency parsing with gnns. @xcite used gnns for dependency parsing. however, they view words instead of edges as gnn nodes. consequently, it is tricky to define neighbors and thus tricky to design node vector update schemes. in our model, we view edges as gnn nodes, so we can define neighbors and design node vector update schemes more naturally by following second-order dependency relationships. in addition, our model captures edge-level features and thus is more expressive. ## conclusion we proposed e-gnns in the spirit of the factor graph representation of second-order dependency parsing. experiments and ablation studies on se-meval 2015 task 18 english datasets of sdp validated the effectiveness of e-gnns.