会话分析中的迁移学习--以预处理数据为监控器的再利用
Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors
摘要：
会话分析系统使用有噪声的人类标签进行训练，并且在多模态特征提取期间通常需要大量的预处理。在单任务学习中使用噪声标签会增加过度拟合的风险。辅助任务可以在相同的训练过程中提高主任务学习的性能-这种方法位于迁移学习和多任务学习（MTL）的交叉点。在本文中，我们探讨了如何预处理的数据用于特征工程可以重复使用作为辅助任务，从而促进数据的生产性使用。我们的主要贡献是：（1）识别出16个有益的辅助任务;（2）研究了主任务和辅助任务之间的学习能力分配方法;（3）研究了主任务和辅助任务之间的相对监督层次。IEMOCAP和SEMAINE数据上的大量实验验证了单任务方法的改进，并表明它可以推广到多个主要任务。
英文摘要：
Conversational analysis systems are trained using noisy human labels and often require heavy preprocessing during multi-modal feature extraction. Using noisy labels in single-task learning increases the risk of over-fitting. Auxiliary tasks could improve the performance of the primary task learning during the same training -- this approach sits in the intersection of transfer learning and multi-task learning (MTL). In this paper, we explore how the preprocessed data used for feature engineering can be re-used as auxiliary tasks, thereby promoting the productive use of data. Our main contributions are: (1) the identification of sixteen beneficially auxiliary tasks, (2) studying the method of distributing learning capacity between the primary and auxiliary tasks, and (3) studying the relative supervision hierarchy between the primary and auxiliary tasks. Extensive experiments on IEMOCAP and SEMAINE data validate the improvements over single-task approaches, and suggest that it may generalize across multiple primary tasks.
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。
