Learn to Combine Linguistic and Symbolic Information for Table-based Fact Verification
Table-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 this work, we propose HeterTFV, a graph-based reasoning approach, that learns to combine linguistic information and symbolic information effectively. We first construct a program graph to encode programs, a kind of LISP-like logical form, to learn the semantic compositionality of the programs. Then we construct a heterogeneous graph to incorporate both linguistic information and symbolic information by introducing program nodes into the heterogeneous graph. Finally, we propose a graph-based reasoning approach to reason over the multiple types of nodes to make an effective combination of both types of information. Experimental results on a large-scale benchmark dataset TABFACT illustrate the effect of our approach.
Code (0)
등록된 구현이 없습니다.
Tasks
Fact VerificationTable-based Fact VerificationSimilar Papers 제목 키워드 기반
Table Fact Verification with Structure-Aware Transformer
Verifying 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 a…
Fact VerificationBeyond Neural Networks: Symbolic Reasoning over Wavelet Logic Graph Signals
We present a fully non neural learning framework based on Graph Laplacian Wavelet Transforms (GLWT). Unlike traditional architectures that rely on convolutional, recurrent, or attention based neural networks, our model o…
Probing Linguistic Information For Logical Inference In Pre-trained Language Models
Progress in pre-trained language models has led to a surge of impressive results on downstream tasks for natural language understanding. Recent work on probing pre-trained language models uncovered a wide range of lingui…
Language ModelingLanguage ModellingNatural Language UnderstandingLILO: Learning Interpretable Libraries by Compressing and Documenting Code
While large language models (LLMs) now excel at code generation, a key aspect of software development is the art of refactoring: consolidating code into libraries of reusable and readable programs. In this paper, we intr…
Code GenerationProgram SynthesisLaMoGen: Language to Motion Generation Through LLM-Guided Symbolic Inference
Human motion is highly expressive and naturally aligned with language, yet prevailing methods relying heavily on joint text-motion embeddings struggle to synthesize temporally accurate, detailed motions and often lack ex…
Motion Synthesis