Can language models (LM) ground question-answering (QA) tasks in the knowledge base via inherent relational reasoning ability? While previous models that use only LMs have seen some success on many QA tasks, more recent methods include knowledge graphs (KG) to complement LMs with their more logic-driven implicit knowledge. However, effectively extracting information from structured data, like KGs, empowers LMs to remain an open question, and current models rely on graph techniques to extract knowledge. In this paper, we propose to solely leverage the LMs to combine the language and knowledge for knowledge based question-answering with flexibility, breadth of coverage and structured reasoning. Specifically, we devise a knowledge construction method that retrieves the relevant context with a dynamic hop, which expresses more comprehensivenes than traditional GNN-based techniques. And we devise a deep fusion mechanism to further bridge the information exchanging bottleneck between the language and the knowledge. Extensive experiments show that our model consistently demonstrates its state-of-the-art performance over CommensenseQA benchmark, showcasing the possibility to leverage LMs solely to robustly ground QA into the knowledge base.

翻译：语言模型能否通过其内在的关系推理能力，将问答任务建立在知识库之上？尽管仅使用语言模型的先前方法已在许多问答任务上取得一定成功，但近期方法引入知识图谱以补充语言模型，利用其更具逻辑性的隐式知识。然而，如何从结构化数据（如知识图谱）中有效提取信息以增强语言模型仍是一个开放性问题，且当前模型依赖于图技术来提取知识。本文提出仅利用语言模型，将语言与知识灵活地结合，实现知识基问答，兼具覆盖广度与结构化推理能力。具体而言，我们设计了一种知识构建方法，通过动态跳数检索相关上下文，其表达能力优于传统基于图神经网络的技术。此外，我们提出一种深度融合机制，以进一步桥接语言与知识之间的信息交换瓶颈。大量实验表明，我们的模型在CommensenseQA基准上持续展现出最先进的性能，展示了仅利用语言模型即可稳健地将问答任务建立在知识库中的可能性。