Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty
Recent work in task-independent graph semantic parsing has shifted from grammar-based symbolic approaches to data-intensive neural approaches, and has shown strong performance on different types of meaning representations. However, it is still unclear that what are the limitations of these neural parsers, and whether these limitations can be compensated by collaborating with symbolic parsers. In this paper, we attempt to answer these questions by taking English Resource Grammar (ERG) parsing as a case study. Specifically, we first develop a state-of-the-art neural ERG parser, and then conduct detailed analyses on fine-grained linguistic phenomena. The results suggest that the neural parser's performance degrades significantly on long-tail examples, while the symbolic parser performs more robustly. To address this, we further propose a collaborative neural-symbolic semantic parsing framework. Specifically, we improve the beam search strategy by designing a decision criterion that incorporates both the model uncertainty about the testing data distribution and the prior knowledge from a symbolic parser. Experimental results show that this collaborative parsing framework can outperform the single neural parser and concretely improve the model's performance on long-tail examples.
Code (0)
등록된 구현이 없습니다.
Tasks
Semantic ParsingSimilar Papers 제목 키워드 기반
Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty
Recent work in task-independent graph semantic parsing has shifted from grammar-based symbolic approaches to neural models, showing strong performance on different types of meaning representations. However, it is still u…
Semantic ParsingNeural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification
Pre-trained seq2seq models excel at graph semantic parsing with rich annotated data, but generalize worse to out-of-distribution (OOD) and long-tail examples. In comparison, symbolic parsers under-perform on population-l…
Semantic ParsingUncertainty QuantificationThe Role of Semantic Parsing in Understanding Procedural Text
In this paper, we investigate whether symbolic semantic representations, extracted from deep semantic parsers, can help reasoning over the states of involved entities in a procedural text. We consider a deep semantic par…
Semantic ParsingSemantic Role LabelingSLING: A framework for frame semantic parsing
We describe SLING, a framework for parsing natural language into semantic frames. SLING supports general transition-based, neural-network parsing with bidirectional LSTM input encoding and a Transition Based Recurrent Un…
Semantic Frame ParsingSemantic ParsingLOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning
Current high-performance semantic segmentation models are purely data-driven sub-symbolic approaches and blind to the structured nature of the visual world. This is in stark contrast to human cognition which abstracts vi…
Inductive LearningSegmentationSemantic ParsingSemantic Segmentation