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Learn to Combine Linguistic and Symbolic Information for Table-based Fact Verification
AbstractTable-based fact verification is expected to perform both linguistic reasoning and symbolic reasoning. Existing methods lack attention to take advantage of the combination of linguistic information and symbolic information. In …...
Table Fact Verification with Structure-Aware Transformer
AbstractVerifying fact on semi-structured evidence like tables requires the ability to encode structural information and perform symbolic reasoning. Pre-trained language models trained on natural language could not be directly applied …...
HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation
AbstractTables are often created with hierarchies, but existing works on table reasoning mainly focus on flat tables and neglect hierarchical tables. Hierarchical tables challenge numerical reasoning by complex hierarchical indexing, …...
Interactive Symbol Grounding with Complex Referential Expressions
AbstractWe present a procedure for learning to ground symbols from a sequence of stimuli consisting of an arbitrarily complex noun phrase (e.g. “all but one green square above both red …...
Empirical Investigation of Neural Symbolic Reasoning Strategies
AbstractNeural reasoning accuracy improves when generating intermediate reasoning steps. However, the source of this improvement is yet unclear. Here, we investigate and factorize the benefit of generating intermediate steps for …...