论文标题
从自然语言反馈的对抗性修改中学习即兴聊天机器人
Learning Improvised Chatbots from Adversarial Modifications of Natural Language Feedback
论文作者
论文摘要
聊天机器人的无处不在性质及其与用户的互动产生大量数据。我们可以使用这些数据改进聊天机器人吗？当用户对其响应不满意时，自然语言反馈通过询问自然语言反馈，并将此反馈用作额外的培训样本，从而改善了自己。但是，在大多数情况下，用户反馈包含无关的序列，以阻碍其作为培训样本的用途。在这项工作中，我们提出了一个生成的对抗模型，该模型将嘈杂的反馈转换为对话中合理的自然响应。发电机的目标是将反馈转换为回应用户以前的话语的响应，并欺骗将反馈与自然响应区分开的歧视者。我们表明，使用这些修改后的反馈响应增强原始培训数据可将原始聊天机器人的性能从69.94％提高到75.96％，从而将正确的响应对人为响应进行排名，鉴于原始模型已经对131K样品进行了培训，因此有很大的改进。
The ubiquitous nature of chatbots and their interaction with users generate an enormous amount of data. Can we improve chatbots using this data? A self-feeding chatbot improves itself by asking natural language feedback when a user is dissatisfied with its response and uses this feedback as an additional training sample. However, user feedback in most cases contains extraneous sequences hindering their usefulness as a training sample. In this work, we propose a generative adversarial model that converts noisy feedback into a plausible natural response in a conversation. The generator's goal is to convert the feedback into a response that answers the user's previous utterance and to fool the discriminator which distinguishes feedback from natural responses. We show that augmenting original training data with these modified feedback responses improves the original chatbot performance from 69.94% to 75.96% in ranking correct responses on the Personachat dataset, a large improvement given that the original model is already trained on 131k samples.
