用神经符号对比学习提升跨域推理能力，兼顾逻辑准确与泛化性。
Neuro-Symbolic Contrastive Learning for Cross-domain Inference
- 将逻辑规则和数据表示为逻辑程序，构建可微的神经符号嵌入空间
- 在稀疏、噪声数据上实现更高逻辑准确性与推理泛化能力
- 适合需要强逻辑推理的跨域自然语言任务
预训练语言模型在自然语言推理任务中虽有进展，但对文本扰动敏感且依赖大规模数据，过度依赖浅层启发式。相反，归纳逻辑编程擅长从多样、稀疏、有限的数据中推断逻辑关系，但其离散特性要求输入精确，应用受限。本文提出神经符号对比学习，实现平滑可微优化，提升在原本离散、噪声大、稀疏的逻辑函数拓扑空间中的逻辑准确性。通过将数据表示为逻辑程序和规则集，有效嵌入抽象逻辑关系，使语义逻辑关系相似的文本信息被聚类，而逻辑关系不同的相似文本被分离。实验表明，该方法显著提升了模型在泛化与推理方面的性能。
原文摘要 · Abstract (English)
Pre-trained language models (PLMs) have made significant advances in natural language inference (NLI) tasks, however their sensitivity to textual perturbations and dependence on large datasets indicate an over-reliance on shallow heuristics. In contrast, inductive logic programming (ILP) excels at inferring logical relationships across diverse, sparse and limited datasets, but its discrete nature requires the inputs to be precisely specified, which limits their application. This paper proposes a bridge between the two approaches: neuro-symbolic contrastive learning. This allows for smooth and differentiable optimisation that improves logical accuracy across an otherwise discrete, noisy, and sparse topological space of logical functions. We show that abstract logical relationships can be effectively embedded within a neuro-symbolic paradigm, by representing data as logic programs and sets of logic rules. The embedding space captures highly varied textual information with similar semantic logical relations, but can also separate similar textual relations that have dissimilar logical relations. Experimental results demonstrate that our approach significantly improves the inference capabilities of the models in terms of generalisation and reasoning.
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