论文标题
通过下一步监督为NLI生成中间步骤
Generating Intermediate Steps for NLI with Next-Step Supervision
论文作者
论文摘要
自然语言推论（NLI）任务通常需要通过多个步骤进行推理才能得出结论。尽管产生此类中间步骤的必要性（而不是摘要说明）获得了大众支持，但尚不清楚如何在没有完整的端到端监督以及如何进一步利用此类生成的步骤的情况下生成此类步骤。在这项工作中，我们训练一个序列到序列模型，仅生成下一步给定NLI前提和假设对（以及以前的步骤）；然后通过外部知识和符号搜索来增强它，以仅在下一步的监督下生成中间步骤。我们通过自动化和人类验证显示了此类生成的步骤的正确性。此外，我们表明，使用简单的数据增强策略在多个公共NLI数据集中使用简单的数据增强策略有助于提高端到端的NLI任务性能。
The Natural Language Inference (NLI) task often requires reasoning over multiple steps to reach the conclusion. While the necessity of generating such intermediate steps (instead of a summary explanation) has gained popular support, it is unclear how to generate such steps without complete end-to-end supervision and how such generated steps can be further utilized. In this work, we train a sequence-to-sequence model to generate only the next step given an NLI premise and hypothesis pair (and previous steps); then enhance it with external knowledge and symbolic search to generate intermediate steps with only next-step supervision. We show the correctness of such generated steps through automated and human verification. Furthermore, we show that such generated steps can help improve end-to-end NLI task performance using simple data augmentation strategies, across multiple public NLI datasets.
