AMR Parsing with Latent Structural Information
Abstract Meaning Representations (AMRs) capture sentence-level semantics structural representations to broad-coverage natural sentences. We investigate parsing AMR with explicit dependency structures and interpretable latent structures. We generate the latent soft structure without additional annotations, and fuse both dependency and latent structure via an extended graph neural networks. The fused structural information helps our experiments results to achieve the best reported results on both AMR 2.0 (77.5{\%} Smatch F1 on LDC2017T10) and AMR 1.0 ((71.8{\%} Smatch F1 on LDC2014T12).
Code (0)
등록된 구현이 없습니다.
Tasks
AMR ParsingSentenceSimilar Papers 제목 키워드 기반
Neural Chinese Address Parsing
This paper introduces a new task {--} Chinese address parsing {--} the task of mapping Chinese addresses into semantically meaningful chunks. While it is possible to model this problem using a conventional sequence label…
Structured PredictionDRTS Parsing with Structure-Aware Encoding and Decoding
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, treatin…
DecoderGraph AttentionSemantic ParsingImproved Latent Tree Induction with Distant Supervision via Span Constraints
For over thirty years, researchers have developed and analyzed methods for latent tree induction as an approach for unsupervised syntactic parsing. Nonetheless, modern systems still do not perform well enough compared to…
Constituency ParsingParsing Videos of Actions with Segmental Grammars
Real-world videos of human activities exhibit temporal structure at various scales; long videos are typically composed out of multiple action instances, where each instance is itself composed of sub-actions with variable…
Incorporating Graph Information in Transformer-based AMR Parsing
Abstract Meaning Representation (AMR) is a Semantic Parsing formalism that aims at providing a semantic graph abstraction representing a given text. Current approaches are based on autoregressive language models such as …
Abstract Meaning RepresentationAMR ParsingKnowledge DistillationSelf-Knowledge Distillation+2