Logical reasoning task involves diverse types of complex reasoning over text,
based on the form of multiple-choice question answering. Given the context,
question and a set of options as the input, previous methods achieve superior
performances on the full-data setting. However, the current benchmark dataset
has the ideal assumption that the reasoning type distribution on the train
split is close to the test split, which is inconsistent with many real
application scenarios. To address it, there remain two problems to be studied:
(1) How is the zero-shot capability of the models (train on seen types and test
on unseen types)? (2) How to enhance the perception of reasoning types for the
models? For problem 1, we propose a new benchmark for generalized zero-shot
logical reasoning, named ZsLR. It includes six splits based on the three type
sampling strategies. For problem 2, a type-aware model TaCo is proposed. It
utilizes both the heuristic input reconstruction and the contrastive learning
to improve the type perception in the global representation. Extensive
experiments on both the zero-shot and full-data settings prove the superiority
of TaCo over the state-of-the-art methods. Also, we experiment and verify the
generalization capability of TaCo on other logical reasoning dataset.

本文探讨了逻辑推理任务中遇到的两个问题：如何提高模型的零样本能力及其推理类型感知能力。作者基于此提出了一个新的基准数据集 ZsLR，并提出了一种类型感知的模型 TaCo，并在多个实验数据集上进行了广泛的实验，证明了 TaCo 比现有方法更为优秀。