Commonsense reasoning is a critical aspect of human communication. Despite recent advances in conversational AI driven by large language models, commonsense reasoning remains a challenging task. In this work, we introduce SYNDICOM - a method for improving commonsense in dialogue response generation. SYNDICOM consists of two components. The first component is a dataset composed of commonsense dialogues created from a knowledge graph and synthesized into natural language. This dataset includes both valid and invalid responses to dialogue contexts, along with natural language feedback (NLF) for the invalid responses. The second contribution is a two-step procedure: training a model to predict natural language feedback (NLF) for invalid responses, and then training a response generation model conditioned on the predicted NLF, the invalid response, and the dialogue. SYNDICOM is scalable and does not require reinforcement learning. Empirical results on three tasks are evaluated using a broad range of metrics. SYNDICOM achieves a relative improvement of 53% over ChatGPT on ROUGE1, and human evaluators prefer SYNDICOM over ChatGPT 57% of the time. We will publicly release the code and the full dataset.

通过引入SYNDICOM方法来提高对话回复生成中的常识推理，该方法包括基于知识图谱创建的常识对话数据集和预测自然语言反馈的模型，以及在预测的自然语言反馈、无效回复和对话的条件下训练响应生成模型。SYNDICOM在多项任务上相对于ChatGPT的ROUGE1指标实现了53%的相对改进，并且57%的人类评估者更喜欢SYNDICOM。

SYNDICOM: 通过错误注入与自然语言反馈提升对话常识