Title: Robust Task Clustering for Deep Many-Task Learning,
Abstract: We investigate task clustering for deep-learning based multi-task and
few-shot learning in a many-task setting. We propose a new method to measure
task similarities with cross-task transfer performance matrix for the deep
learning scenario. Although this matrix provides us critical information
regarding similarity between tasks, its asymmetric property and unreliable
performance scores can affect conventional clustering methods adversely.
Additionally, the uncertain task-pairs, i.e., the ones with extremely
asymmetric transfer scores, may collectively mislead clustering algorithms to
output an inaccurate task-partition. To overcome these limitations, we propose
a novel task-clustering algorithm by using the matrix completion technique. The
proposed algorithm constructs a partially-observed similarity matrix based on
the certainty of cluster membership of the task-pairs. We then use a matrix
completion algorithm to complete the similarity matrix. Our theoretical
analysis shows that under mild constraints, the proposed algorithm will
perfectly recover the underlying "true" similarity matrix with a high
probability. Our results show that the new task clustering method can discover
task clusters for training flexible and superior neural network models in a
multi-task learning setup for sentiment classification and dialog intent
classification tasks. Our task clustering approach also extends metric-based
few-shot learning methods to adapt multiple metrics, which demonstrates
empirical advantages when the tasks are diverse.