We present an end-to-end approach for knowledge-grounded response generation in Dialog System Technology Challenges 7 (DSTC7). Our system is trained by a pointer generator model, so that an output token in a response can either be generated or copied from conversation history or facts according to a trainable action probability distribution. Furthermore, to minimize generating meaningless responses, we propose using K-means to dynamically cluster and prune semantically similar partial hypotheses at each decoding step under a fixed beam budget. Moreover, we employ a language model to filter meaningless responses. Official evaluation results show that our proposed system achieved the first place in all primary automatic evaluation metrics and the overall human evaluation score.

我们在Dialog System Technology Challenges 7（DSTC7）中提出了一种基于知识的响应生成的端到端方法。我们的系统由指针生成器模型训练，因此可以根据可训练的动作概率分布从对话历史或事实中生成或复制响应中的输出令牌。此外，为了最小化产生无意义的响应，我们建议使用K-means在固定波束预算下的每个解码步骤中动态聚类和删减语义相似的部分假设。此外，我们采用语言模型来过滤无意义的响应。官方评估结果表明，我们提出的系统在所有主要的自动评估指标和总体人​​类评估得分中均排名第一。