A Globally Normalized Neural Model for Semantic Parsing
In this paper, we propose a globally normalized model for context-free grammar (CFG)-based semantic parsing. Instead of predicting a probability, our model predicts a real-valued score at each step and does not suffer from the label bias problem. Experiments show that our approach outperforms locally normalized models on small datasets, but it does not yield improvement on a large dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
modelSemantic ParsingSimilar Papers 제목 키워드 기반
Globally Normalized Transition-Based Neural Networks
We introduce a globally normalized transition-based neural network model that achieves state-of-the-art part-of-speech tagging, dependency parsing and sentence compression results. Our model is a simple feed-forward neur…
Dependency ParsingPart-Of-Speech TaggingSentenceSentence CompressionCRF Autoencoder for Unsupervised Dependency Parsing
Unsupervised dependency parsing, which tries to discover linguistic dependency structures from unannotated data, is a very challenging task. Almost all previous work on this task focuses on learning generative models. In…
Dependency Grammar InductionUnsupervised Dependency ParsingA Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese
Semantic parsing is an important NLP task. However, Vietnamese is a low-resource language in this research area. In this paper, we present the first public large-scale Text-to-SQL semantic parsing dataset for Vietnamese.…
Language ModelingLanguage ModellingSemantic ParsingText to SQL+3Dependency Parsing with Partial Annotations: An Empirical Comparison
This paper describes and compares two straightforward approaches for dependency parsing with partial annotations (PA). The first approach is based on a forest-based training objective for two CRF parsers, i.e., a biaffin…
Active LearningDependency ParsingNon-Projective Dependency Parsing via Latent Heads Representation (LHR)
In this paper, we introduce a novel approach based on a bidirectional recurrent autoencoder to perform globally optimized non-projective dependency parsing via semi-supervised learning. The syntactic analysis is complete…
Dependency Parsing