基于半监督学习的反向信道预测算法研究
Exploring Semi-Supervised Learning for Predicting Listener Backchannels
摘要：
开发类人会话代理是HCI研究的主要领域，包含许多任务。预测听众反馈通道就是这样一个积极研究的任务。虽然许多研究使用不同的方法进行反向通道预测，但它们都依赖于大型数据集的手动注释。这是影响开发可伸缩性的瓶颈。为此，我们建议使用半监督技术来自动化识别反向通道的过程，从而简化注释过程。为了分析我们的识别模块的可行性，我们比较了在（a）手动注释和（b）半监督标签上训练的反向通道预测模型。定量分析表明，所提出的半监督方法可以达到前者的95%的性能。我们的用户研究结果显示，近60%的参与者发现所提出的模型预测的反向通道反应更自然。最后，我们还分析了人格对反向通道信号类型的影响，并在用户研究中验证了我们的发现。
英文摘要：
Developing human-like conversational agents is a prime area in HCI research and subsumes many tasks. Predicting listener backchannels is one such actively-researched task. While many studies have used different approaches for backchannel prediction, they all have depended on manual annotations for a large dataset. This is a bottleneck impacting the scalability of development. To this end, we propose using semi-supervised techniques to automate the process of identifying backchannels, thereby easing the annotation process. To analyze our identification module's feasibility, we compared the backchannel prediction models trained on (a) manually-annotated and (b) semi-supervised labels. Quantitative analysis revealed that the proposed semi-supervised approach could attain 95% of the former's performance. Our user-study findings revealed that almost 60% of the participants found the backchannel responses predicted by the proposed model more natural. Finally, we also analyzed the impact of personality on the type of backchannel signals and validated our findings in the user-study.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
