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Current self-training methods such as standard self-training, co-training,tri-training, and others often focus on improving model performance on a singletask, utilizing differences in input features, model architectures, andtraining processes. However, many tasks in natural language processing areabout different but related aspects of language, and models trained for onetask can be great teachers for other related tasks. In this work, we proposefriend-training, a cross-task self-training framework, where models trained todo different tasks are used in an iterative training, pseudo-labeling, andretraining process to help each other for better selection of pseudo-labels.With two dialogue understanding tasks, conversational semantic role labelingand dialogue rewriting, chosen for a case study, we show that the modelstrained with the friend-training framework achieve the best performancecompared to strong baselines.

Friend-training: Learning from Models of Different but Related Tasks