Large, transformer-based pretrained language models like BERT, GPT, and T5 have demonstrated a deep understanding of contextual semantics and language syntax. Their success has enabled significant advances in conversational AI, including the development of open-dialogue systems capable of coherent, salient conversations which can answer questions, chat casually, and complete tasks. However, state-of-the-art models still struggle with tasks that involve higher levels of reasoning - including commonsense reasoning that humans find trivial. This paper presents a survey of recent conversational AI research focused on commonsense reasoning. The paper lists relevant training datasets and describes the primary approaches to include commonsense in conversational AI. The paper also discusses benchmarks used for evaluating commonsense in conversational AI problems. Finally, the paper presents preliminary observations of the limited commonsense capabilities of two state-of-the-art open dialogue models, BlenderBot3 and LaMDA, and its negative effect on natural interactions. These observations further motivate research on commonsense reasoning in conversational AI.

翻译：基于BERT、GPT和T5等Transformer架构的大规模预训练语言模型已展现出对上下文语义和语言句法的深刻理解。这一成功推动了对话式AI的重大进展，包括开发出能够进行连贯、有意义对话的开放式对话系统，这些系统可以回答问题、随意聊天并完成任务。然而，最先进的模型在处理涉及更高层次推理的任务时仍存在困难——包括人类认为微不足道的常识推理。本文对近期聚焦常识推理的对话式AI研究进行了综述。文中列举了相关训练数据集，描述了在对话式AI中融入常识的主要方法，讨论了用于评估对话式AI问题中常识推理能力的基准测试，最后初步观察了两个最先进的开放式对话模型BlenderBot3和LaMDA在常识推理方面的局限性及其对自然交互的负面影响。这些观察结果进一步激发了对话式AI中常识推理的研究动力。