论文标题
预测和使用潜在模式进行短文本对话
Predict and Use Latent Patterns for Short-Text Conversation
论文作者
论文摘要
如今，许多神经网络模型都在聊天环境中取得了有希望的表演。他们中的大多数依靠编码器来理解帖子和解码器来产生响应。如果没有给定的分配的语义，模型就缺乏对响应的细粒度控制，因为帖子和响应之间的语义映射在端到端的举止中被隐藏了。以前的一些作品利用采样的潜在单词作为可控的语义形式来推动围绕工作的生成的响应，但是很少有作品试图使用更复杂的语义模式来指导生成。在本文中，我们建议使用更详细的语义形式，包括从相应分布中采样的潜在响应和一部分语音序列，作为可控语义来指导一代。我们的结果表明，更丰富的语义不仅能够提供信息丰富和多样化的响应，而且还提高了响应质量的整体性能，包括流利性和连贯性。
Many neural network models nowadays have achieved promising performances in Chit-chat settings. The majority of them rely on an encoder for understanding the post and a decoder for generating the response. Without given assigned semantics, the models lack the fine-grained control over responses as the semantic mapping between posts and responses is hidden on the fly within the end-to-end manners. Some previous works utilize sampled latent words as a controllable semantic form to drive the generated response around the work, but few works attempt to use more complex semantic patterns to guide the generation. In this paper, we propose to use more detailed semantic forms, including latent responses and part-of-speech sequences sampled from the corresponding distributions, as the controllable semantics to guide the generation. Our results show that the richer semantics are not only able to provide informative and diverse responses, but also increase the overall performance of response quality, including fluency and coherence.
