Along with the development of social media on the internet, dialogue systems are becoming more and more intelligent to meet users’ needs for communication, emotion, and social intercourse. Previous studies usually use sequence-to-sequence learning with recurrent neural networks for response generation. However, recurrent-based learning models heavily suffer from the problem of long-distance dependencies in sequences. Moreover, some models neglect crucial information in the dialogue contexts, which leads to uninformative and inflexible responses. To address these issues, we present a bichannel transformer with context encoding (BCTCE) for document-driven conversation. This conversational generator consists of a context encoder, an utterance encoder, and a decoder with attention mechanism. The encoders aim to learn the distributed representation of input texts. The multihop attention mechanism is used in BCTCE to capture the interaction between documents and dialogues. We evaluate the proposed BCTCE by both automatic evaluation and human judgment. The experimental results on the dataset CMU_DoG indicate that the proposed model yields significant improvements over the state-of-the-art baselines on most of the evaluation metrics, and the generated responses of BCTCE are more informative and more relevant to dialogues than baselines.

中文翻译：

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具有上下文编码的双通道变压器，用于社交媒体中文档驱动的对话生成

随着互联网上社交媒体的发展，对话系统变得越来越智能，以满足用户对交流，情感和社交的需求。先前的研究通常将序列到序列学习与递归神经网络一起用于生成响应。然而，基于递归的学习模型遭受序列中的长距离依赖性的问题。此外，一些模型在对话环境中忽略了关键信息，这导致了缺乏信息性和僵化的反应。为了解决这些问题，我们提出了一种带上下文编码的双通道转换器（BCTCE），用于文档驱动的对话。该会话生成器由上下文编码器，话语编码器和具有注意机制的解码器组成。编码器旨在学习输入文本的分布式表示。BCTCE中使用了多跳注意机制来捕获文档和对话之间的交互。我们通过自动评估和人工判断来评估提议的BCTCE。数据集CMU_DoG上的实验结果表明，在大多数评估指标上，所提出的模型均比最新的基线有了显着改善，并且BCTCE的响应比基线更能提供更多信息，并且与对话更加相关。