UC Davis at SemEval-2019 Task 1: DAG Semantic Parsing with Attention-based Decoder
Dian Yu, Kenji Sagae · 2019
We present a simple and accurate model for semantic parsing with UCCA as our submission for SemEval 2019 Task 1.We propose an encoder-decoder model that maps strings to directed acyclic graphs.Unlike many transitionbased approaches, our approach does not use a state representation, and unlike graph-based parsers, it does not score graphs directly.Instead, we encode input sentences with a bidirectional-LSTM, and decode with selfattention to build a graph structure.Results show that our parser is simple and effective for semantic parsing with reentrancy and discontinuous structures.