用于多样化对话响应生成的解码器混合模型
Mixture of Decoders for Diverse Dialog Response Generation
浏览论文内容
中文总结 AI 辅助
针对对话响应生成多样性不足的问题，提出在CVAE框架下将多个解码器混合融入序列到序列模型，使各解码器学习专门主题，在开放领域聊天语料上经定量与人工评估验证了有效性。
中文摘要 AI 辅助
混合建模是一种长期建立的机器学习技术，用于学习大规模多模态数据。虽然已知用于对话响应生成的序列到序列模型存在低多样性问题，但我们假设这是因为序列到序列模型倾向于学习退化的单峰响应分布。因此，我们提出将解码器混合模型融入序列到序列模型，并尝试让每个解码器学习专门的主题，以提高生成响应的多样性。我们的模型是在条件变分自编码器（CVAE）框架下开发的。我们在一个开放领域的聊天语料库上评估了我们的方法，并在定量指标和人工评估方面显示出相对于强基线的改进。
英文摘要
Mixture modeling is a long established machine learning technique for learning large sets of multi-modal data. While it is known that sequence-to-sequence models for dialog response generation suffer from the problem of low diversity, we hypothesize that it is because sequence-to-sequence models tend to learn a degenerate uni-modal distribution of responses. We then propose to incorporate a mixture of decoders into sequence-to-sequence models and try to make each decoder learn specialized topics in order to improve the diversity of generated responses. Our model is developed under the framework of conditional variational autoencoder (CVAE). We evaluate our approach on an open domain chat corpus and show improvement over strong baselines in quantitative measures and human evaluation.
发表机构
- Microsoft Corporation（微软公司）
机构由 AI 辅助整理，请以论文原文为准。
