Weakly Supervised Domain Detection

Weakly Supervised Domain Detection Yumo Xu and Mirella Lapata Institute for Language, Cognition and Computation School of Informatics, University of Edinburgh 10 Crichton Street, Edinburgh EH8 9AB yumo.xu@ed.ac.uk, mlap@inf.ed.ac.uk Abstract In this paper we introduce domain detection as a new natural language processing task. We argue that the ability to detect textual seg- ments that are domain-heavy (i.e., sentences or phrases that are representative of and provide evidence for a given domain) could enhance the robustness and portability of various text clas- sification applications. We propose an encoder- detector framework for domain detection and bootstrapclassifierswithmultiple instance learn- ing. The model is hierarchically organized and suited to multilabel classification. We demon- strate that despite learning with minimal super- vision, our model can be applied to text spans of different granularities, languages, and gen- res. We also showcase the potential of domain detection for text summarization. 1 Introduction Text classification is a fundamental task in Natural Language Processing (NLP) that has been found useful in a wide spectrum of applications ranging from search engines enabling users to identify content on Web sites, sentiment and social media analysis, customer relationship man- agement systems, and spam detection. Over the past several years, text classification has been pre- dominantly modeled as a supervised learning problem (e.g., Kim, 2014; McCallum and Nigam, 1998; Iyyer et al., 2015) for which appropriately labeled data must be collected. Such data are often domain-dependent (i.e., covering specific topics such as those relating to ‘‘Business’’ or ‘‘Medicine’’) and a classifier trained using data from one domain is likely to perform poorly on another. For example, the phrase ‘‘the mouse died quickly’’ may indicate negative sentiment in a customer review describing the hand-held pointing device or positive sentiment when describing a laboratory experiment performed on a rodent. The ability to handle a wide variety of domains1 has become more pertinent with the rise of data-hungry machine learning techniques like neural networks and their application to a plethora of textual media ranging from news articles to Twitter, blog posts, medical journals, Reddit comments, and parliamentary debates (Kim, 2014; Yang et al., 2016; Conneau et al., 2017; Zhang et al., 2016). The question of how to best deal with multiple domains when training data are available for one or few of them has met with much interest in the literature. The field of domain adaptation (Jiang and Zhai, 2007; Blitzer et al., 2006; Daume III, 2007; Finkel and Manning, 2009; Lu et al., 2016) aims at improving the learning of a predictive function in a target domain where there is little or no labeled data, using knowledge transferred from a source domain where sufficient labeled data are available. Another line of work (Li and Zong, 2008; Wu and Huang, 2015; Chen and Cardie, 2018) assumes that labeled data may exist for multiple domains, but in insufficient amounts to train classifiers for one or more of them. The aim of multi-domain text classification is to leverage all the available resources in order to improve system …

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