论文标题
通过多任务学习在开放域对话中选择贴纸
Selecting Stickers in Open-Domain Dialogue through Multitask Learning
论文作者
论文摘要
随着在线聊天的日益普及，贴纸在我们的在线沟通中变得越来越重要。在开放域对话中选择适当的贴纸需要对对话和贴纸以及两种类型的方式之间的关系有全面的了解。为了应对这些挑战，我们提出了一种由三个辅助任务组成的多任务学习方法，以增强对对话历史，情感和语义含义的理解。在最近的一个挑战性数据集中进行的广泛实验表明，我们的模型可以更好地结合多模式信息，并在强大的基线上获得更高的精度。消融研究进一步验证了每个辅助任务的有效性。我们的代码可在\ url {https://github.com/nonstopfor/sticker-selection}中找到
With the increasing popularity of online chatting, stickers are becoming important in our online communication. Selecting appropriate stickers in open-domain dialogue requires a comprehensive understanding of both dialogues and stickers, as well as the relationship between the two types of modalities. To tackle these challenges, we propose a multitask learning method comprised of three auxiliary tasks to enhance the understanding of dialogue history, emotion and semantic meaning of stickers. Extensive experiments conducted on a recent challenging dataset show that our model can better combine the multimodal information and achieve significantly higher accuracy over strong baselines. Ablation study further verifies the effectiveness of each auxiliary task. Our code is available at \url{https://github.com/nonstopfor/Sticker-Selection}
