Cross-domain Text Classification with Multiple Domains and Disparate Label Sets

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 1641–1650, Berlin, Germany, August 7-12, 2016. c⃝2016 Association for Computational Linguistics Cross-domain Text Classification with Multiple Domains and Disparate Label Sets Himanshu S. Bhatt, Manjira Sinha and Shourya Roy Xerox Research Centre India, Bangalore, INDIA {Firstname.Lastname}@Xerox.Com Abstract Advances in transfer learning have let go the limitations of traditional supervised machine learning algorithms for being de- pendent on annotated training data for training new models for every new do- main. However, several applications en- counter scenarios where models need to transfer/adapt across domains when the la- bel sets vary both in terms of count of la- bels as well as their connotations. This pa- per presents first-of-its-kind transfer learn- ing algorithm for cross-domain classifica- tion with multiple source domains and dis- parate label sets. It starts with identifying transferable knowledge from across multi- ple domains that can be useful for learning the target domain task. This knowledge in the form of selective labeled instances from different domains is congregated to form an auxiliary training set which is used for learning the target domain task. Experimental results validate the efficacy of the proposed algorithm against strong baselines on a real world social media and the 20 Newsgroups datasets. 1 Introduction A fundamental assumption in supervised statisti- cal learning is that training and test data are inde- pendently and identically distributed (i.i.d.) sam- ples drawn from a distribution. Otherwise, good performance on test data cannot be guaranteed even if the training error is low. On the other hand, transfer learning techniques allow domains, tasks, and distributions used in training and testing to be different, but related. It works in contrast to traditional supervised techniques on the principle of transferring learned knowledge across domains. Pan and Yang, in their survey paper (2010), de- Figure 1: Cross-domain (a) sentiment classifica- tion and (b) subject classification. Illustrates (a) invariant and (b) disparate label sets. scribed different transfer learning settings depend- ing on if domains and tasks vary as well as labeled data is available in one/more/none of the domains. In this paper, we propose a generic solution for multi-source transfer learning where domains and tasks are different and no labeled data is available in the target domain. This is a relatively less char- tered territory and arguably a more generic setting of transfer learning. Motivating example: Consider a social media consulting company helping brands to monitor their social media channels. Two problems typi- cally of interest are: (i) sentiment classification (is a post positive/negative/neutral?) and (ii) subject classification (what was the subject of a post?). While sentiment classification attempts to classify a post based on its polarity, subject classification is towards identifying the subject (or topic) of the post, as illustrated in Figure 1. The company has been using standard classification techniques from an off-the-shelf machine learning toolbox. While machine learning toolkit helps them to cre- ate and apply statistical models efficiently, the same model can not be applied on a new collec- …

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