Controlling chatbot utterance generation with multiple attributes such as personalities, emotions and dialogue acts is a practically useful but under-studied problem. We propose a novel framework called DASC that possesses strong controllability with a weighted decoding paradigm, while improving generation quality with the grounding in an attribute semantics space. Generation with multiple attributes is then intuitively implemented with an interpolation of multiple attribute embeddings, which results in substantial reduction in the model sizes. Experiments show that DASC can achieve high control accuracy in generation task with the simultaneous control of 3 aspects while also producing interesting and reasonably sensible responses, even in an out-of-distribution robustness test.

翻译：控制聊天机器人生成具有多属性（如个性、情感和对话行为）的话语是一个实用但研究不足的问题。我们提出了一种名为DASC的新框架，该框架通过加权解码范式具备强大的可控性，同时通过在属性语义空间中进行基础化处理来提升生成质量。多属性生成通过多个属性嵌入的插值直观实现，从而大幅减小模型规模。实验表明，DASC能够在同时控制3个方面的生成任务中实现高控制精度，即使在分布外鲁棒性测试中也能生成有趣且合理响应的内容。