Neural generative models have become popular and achieved promising performance on short-text conversation tasks. They are generally trained to build a 1-to-1 mapping from the input post to its output response. However, a given post is often associated with multiple replies simultaneously in real applications. Previous research on this task mainly focuses on improving the relevance and informativeness of the top one generated response for each post. Very few works study generating multiple accurate and diverse responses for the same post. In this paper, we propose a novel response generation model, which considers a set of responses jointly and generates multiple diverse responses simultaneously. A reinforcement learning algorithm is designed to solve our model. Experiments on two short-text conversation tasks validate that the multiple responses generated by our model obtain higher quality and larger diversity compared with various state-of-the-art generative models.
翻译:神经基因模型已变得受欢迎,在短文本对话任务上取得了有希望的成绩,它们通常经过培训,从输入站到其输出响应进行一到一的绘图;然而,一个特定职位往往与实际应用中同时的多重答复相关。以前关于这项任务的研究主要侧重于提高每个职位最高级响应的关联性和信息性。很少有研究为同一职位产生多重准确和多样的响应。我们在本文件中提出了一个新的反应生成模型,它考虑到一套联合反应,同时产生多种响应。强化学习算法旨在解决我们的模型。关于两个短文本对话任务的实验证实,与各种最先进的基因模型相比,我们模型产生的多重响应获得了更高的质量和更大的多样性。