Lost in Transduction: Transductive Transfer Learning in Text Classification

Obtaining high-quality labelled data for training a classifier in a new application domain is often costly.  Transfer Learning (a.k.a. “Inductive Transfer“) tries to alleviate these costs by transferring, to the “target“ domain of interest, knowledge available from a different “source“ domain.  In transfer learning the lack of labelled information from the target domain is compensated by the availability at training time of a set of unlabelled examples from the target distribution. Transductive Transfer Learning denotes the transfer learning setting in which the only set of target documents that we are interested in classifying is known and available at training time. Although this definition is indeed in line with Vapnik's original definition of “transduction“, current terminology in the field is confused.  In this article we discuss how the term “transduction“ has been misused in the transfer learning literature, and propose a clarification consistent with the original characterization of this term given by Vapnik. We go on to observe that the above terminology misuse has brought about misleading experimental comparisons, with inductive transfer learning methods that have been incorrectly compared with transductive transfer learning methods. We then give empirical evidence that the difference in performance between the inductive version and the transductive version of a transfer learning method can indeed be statistically significant (i.e., that knowing at training time the only data one needs to classify indeed gives an advantage).  Our clarification allows a reassessment of the field, and of the relative merits of the major, state-of-the-art algorithms for transfer learning in text classification.