Weakly Supervised Domain Detection

Abstract

In this paper we introduce domain detection as a new natural language processing task. We argue that the ability to detect textual segments which are domain-heavy, i.e., sentences or phrases which are representative of and provide evidence for a given domain could enhance the robustness and portability of various text classification applications. We propose an encoder-detector framework for domain detection and bootstrap classifiers with multiple instance learning (MIL). The model is hierarchically organized and suited to multilabel classification. We demonstrate that despite learning with minimal supervision, our model can be applied to text spans of different granularities, languages, and genres. We also showcase the potential of domain detection for text summarization.

To appear in Transactions of the Association for Computational Linguistics (TACL); 16 pages

References in corpus (6)

• Frustratingly Easy Domain Adaptation
• Supervised Topic Models
• Domain Separation Networks
• QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
• Variations of the Similarity Function of TextRank for Automated Summarization
• Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network