让自然语言推理的逻辑表达更准确，提升自动证明的保真率。
Entailment-Preserving First-order Logic Representations in Natural Language Entailment
- 设计新任务与评估指标，直接优化逻辑表达对蕴含关系的保持能力。
- 提出迭代学习排序方法，在三个数据集上使保真率提升1.8%-2.7%。
- 有效减少逻辑表达的随意性，适用于多种推理类型和跨领域数据。
一阶逻辑（FOL）可表征自然语言句子间的逻辑蕴含语义，但利用FOL判断自然语言蕴含仍具挑战。为此，我们提出蕴含保持型一阶逻辑表示任务（EPF），并引入无参考评估指标——蕴含保持率（EPR）家族。在EPF中，需从多前提自然语言蕴含数据（如EntailmentBank）生成FOL表示，使自动证明器结果保持原始蕴含标签。实验表明，现有NL到FOL转换方法在该任务上表现不佳。为此，我们提出一种专用于EPF的训练方法：迭代学习排序，通过新型评分函数与学习排序目标直接优化模型的EPR得分。该方法在三个数据集上实现1.8%-2.7%的EPR提升，以及17.4%-20.6%的EPR@16增长。进一步分析显示，该方法有效抑制了FOL表示的任意性，降低谓词签名多样性，并在多种推理类型及域外数据上保持强性能。
原文摘要 · Abstract (English)
First-order logic (FOL) can represent the logical entailment semantics of natural language (NL) sentences, but determining natural language entailment using FOL remains a challenge. To address this, we propose the Entailment-Preserving FOL representations (EPF) task and introduce reference-free evaluation metrics for EPF, the Entailment-Preserving Rate (EPR) family. In EPF, one should generate FOL representations from multi-premise natural language entailment data (e.g. EntailmentBank) so that the automatic prover's result preserves the entailment labels. Experiments show that existing methods for NL-to-FOL translation struggle in EPF. To this extent, we propose a training method specialized for the task, iterative learning-to-rank, which directly optimizes the model's EPR score through a novel scoring function and a learning-to-rank objective. Our method achieves a 1.8-2.7% improvement in EPR and a 17.4-20.6% increase in EPR@16 compared to diverse baselines in three datasets. Further analyses reveal that iterative learning-to-rank effectively suppresses the arbitrariness of FOL representation by reducing the diversity of predicate signatures, and maintains strong performance across diverse inference types and out-of-domain data.
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