Fact verification systems assess a claim's veracity based on evidence. An
important consideration in designing them is faithfulness, i.e. generating
explanations that accurately reflect the reasoning of the model. Recent works
have focused on natural logic, which operates directly on natural language by
capturing the semantic relation of spans between an aligned claim with its
evidence via set-theoretic operators. However, these approaches rely on
substantial resources for training, which are only available for high-resource
languages. To this end, we propose to use question answering to predict natural
logic operators, taking advantage of the generalization capabilities of
instruction-tuned language models. Thus, we obviate the need for annotated
training data while still relying on a deterministic inference system. In a
few-shot setting on FEVER, our approach outperforms the best baseline by $4.3$
accuracy points, including a state-of-the-art pre-trained seq2seq natural logic
system, as well as a state-of-the-art prompt-based classifier. Our system
demonstrates its robustness and portability, achieving competitive performance
on a counterfactual dataset and surpassing all approaches without further
annotation on a Danish verification dataset. A human evaluation indicates that
our approach produces more plausible proofs with fewer erroneous natural logic
operators than previous natural logic-based systems.

使用问题回答预测自然逻辑运算符，不依赖标注数据、具有确定性推理系统的方法，在少样本设置下，在 FEVER 上的准确度比最佳基线提高了 4.3 个百分点，并且在丹麦验证数据集上超过了所有其他方法，展示了系统的鲁棒性和可移植性，通过人工评估得出结论：相较于之前的基于自然逻辑的系统，我们的方法产生了更可信的证据，并且使用更少错误的自然逻辑运算符。