UC Davis at SemEval-2019 task 1: DAG semantic parsing with attention-based decoder

Abstract

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 transition-based 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 self-attention to build a graph structure. Results show that our parser is simple and effective for semantic parsing with reentrancy and discontinuous structures.