Context-Controlled Topic-Aware Neural Response Generation for Open-Domain Dialog Systems

Context-Controlled Topic-Aware Neural Response Generation for Open-Domain Dialog Systems

Incorporating topic information can help response generation models to produce informative responses for chat-bots. Previous work only considers the individual semantic of each topic, ignoring its specific dialog context, which may result in inaccurate topic representation and hurt response coherence. Besides, as an important feature of multi-turn conversation, dynamic topic transitions have not been well-studied. We propose a Context-Controlled Topic-Aware neural response generation model, i.e., CCTA, which makes dialog context interact with the process of topic representing and transiting to achieve balanced improvements on response informativeness and contextual coherence. CCTA focuses on capturing the semantical relations within topics as well as their corresponding contextual information in conversation, to produce context-dependent topic representations at the word-level and turn-level. Besides, CCTA introduces a context controlled topic transition strategy, utilizing contextual topics to yield relevant transition words. Extensive experimental results on two benchmark multi-turn conversation datasets validate the superiority of our proposal on generating coherent and informative responses against the state-ofthe-art baselines. We also find that topic transition modeling can work as an auxiliary learning task to boost the response generation.

融入主题信息有助于回复生成模型为聊天机器人生成内容丰富的回复。先前的工作仅考虑每个主题的个体语义，忽略了其特定的对话语境，这可能导致主题表示不准确，并损害回复的连贯性。此外，作为多轮对话的一个重要特征，动态主题转换尚未得到充分研究。我们提出了一种语境控制的主题感知神经回复生成模型，即CCTA，它使对话语境与主题表示和转换过程相互作用，以在回复的信息量和语境连贯性方面实现平衡的提升。CCTA专注于捕捉主题内的语义关系以及它们在对话中相应的语境信息，以便在单词级别和轮次级别生成依赖于语境的主题表示。此外，CCTA引入了一种语境控制的主题转换策略，利用语境主题生成相关的转换词。在两个基准多轮对话数据集上的大量实验结果验证了我们的方案相对于最先进的基线在生成连贯且信息丰富的回复方面的优越性。我们还发现主题转换建模可以作为一种辅助学习任务来促进回复生成。