An interaction-modeling mechanism for context-dependent Text-to-SQL translation based on heterogeneous graph aggregation

Yu, Wei; Chang, Tao; Guo, Xiaoting; Wang, Mengzhu; Wang, Xiaodong

doi:10.1016/j.neunet.2021.07.014

Cited by 8 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Graph neural networks (GNNs) inspire us to posit that operating on graphs and manipulating the structured knowledge can support relational reasoning [7,8] in a sophisticated and flexible pattern, similar to the implementation of grandmother cells regarding the cells as nodes in the graph and collecting evidence in multi-classified aspects of node representations. Further, spatial graph attention networks (GATs) perform the selectivity in reasoning evidence graphs in the manner of grandmother cells using attention mechanisms.…”

Section: Introductionmentioning

confidence: 99%

ClueReader: Heterogeneous Graph Attention Network for Multi-Hop Machine Reading Comprehension

Gao

Wang

et al. 2023

Electronics

View full text Add to dashboard Cite

Multi-hop machine reading comprehension is a challenging task in natural language processing as it requires more reasoning ability across multiple documents. Spectral models based on graph convolutional networks have shown good inferring abilities and lead to competitive results. However, the analysis and reasoning of some are inconsistent with those of humans. Inspired by the concept of grandmother cells in cognitive neuroscience, we propose a heterogeneous graph attention network model named ClueReader to imitate the grandmother cell concept. The model is designed to assemble the semantic features in multi-level representations and automatically concentrate or alleviate information for reasoning through the attention mechanism. The name ClueReader is a metaphor for the pattern of the model: it regards the subjects of queries as the starting points of clues, takes the reasoning entities as bridge points, considers the latent candidate entities as grandmother cells, and the clues end up in candidate entities. The proposed model enables the visualization of the reasoning graph, making it possible to analyze the importance of edges connecting entities and the selectivity in the mention and candidate nodes, which is easier to comprehend empirically. Evaluations on the open-domain multi-hop reading dataset WikiHop and drug–drug interaction dataset MedHop proved the validity of ClueReader and showed the feasibility of its application of the model in the molecular biology domain.

show abstract

Section: Introductionmentioning

confidence: 99%