18 research outputs found
Fast semantic parsing with well-typedness guarantees
AM dependency parsing is a linguistically principled method for neural
semantic parsing with high accuracy across multiple graphbanks. It relies on a
type system that models semantic valency but makes existing parsers slow. We
describe an A* parser and a transition-based parser for AM dependency parsing
which guarantee well-typedness and improve parsing speed by up to 3 orders of
magnitude, while maintaining or improving accuracy.Comment: Accepted at EMNLP 2020, camera-ready versio
Semantic Graph Parsing with Recurrent Neural Network DAG Grammars
Semantic parses are directed acyclic graphs (DAGs), so semantic parsing
should be modeled as graph prediction. But predicting graphs presents difficult
technical challenges, so it is simpler and more common to predict the
linearized graphs found in semantic parsing datasets using well-understood
sequence models. The cost of this simplicity is that the predicted strings may
not be well-formed graphs. We present recurrent neural network DAG grammars, a
graph-aware sequence model that ensures only well-formed graphs while
sidestepping many difficulties in graph prediction. We test our model on the
Parallel Meaning Bank---a multilingual semantic graphbank. Our approach yields
competitive results in English and establishes the first results for German,
Italian and Dutch.Comment: 9 pages, to appear in EMNLP201
Neural Combinatory Constituency Parsing
東京都立大学Tokyo Metropolitan University博士(情報科学)doctoral thesi