Papers with end-to-end parser

2 papers
End-to-End Graph-Based TAG Parsing with Neural Networks (N18-1)

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Challenge: Using BiLSTMs, highway connections, and character-level CNNs, we propose a graph-based Tree Adjoining Grammar (TAG) parser.
Approach: They propose a graph-based Tree Adjoining Grammar parser that uses BiLSTMs, highway connections, and character-level CNNs.
Outcome: The proposed parser outperforms the previously reported best by more than 2.2 LAS and UAS points.
Better Transition-Based AMR Parsing with a Refined Search Space (D18-1)

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Challenge: Abstract Meaning Representation (AMR) parsers require a pipeline approach to learn concepts and relationships.
Approach: They propose to use a transition-based search space to conduct a new compact AMR graph and an improved oracle to perform the search.
Outcome: The proposed system achieves the state-of-the-art performance on various datasets with minimal additional information.

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