DRTS Parsing with Structure-Aware Encoding and Decoding
This work addresses a novel semantic parsing task for natural language processing, but it appears incremental as it builds on existing neural sequence-to-sequence models by incorporating structural details.
The paper tackles the problem of discourse representation tree structure (DRTS) parsing by proposing a structure-aware model that integrates structural information using graph attention networks (GAT), achieving state-of-the-art performance on a benchmark dataset.
Discourse representation tree structure (DRTS) parsing is a novel semantic parsing task which has been concerned most recently. State-of-the-art performance can be achieved by a neural sequence-to-sequence model, treating the tree construction as an incremental sequence generation problem. Structural information such as input syntax and the intermediate skeleton of the partial output has been ignored in the model, which could be potentially useful for the DRTS parsing. In this work, we propose a structural-aware model at both the encoder and decoder phase to integrate the structural information, where graph attention network (GAT) is exploited for effectively modeling. Experimental results on a benchmark dataset show that our proposed model is effective and can obtain the best performance in the literature.