With the advent of neural models, there has been a rapid move away from feature engineering, or at best, simplistically combining hand-crafted features with learned representations as side information. We propose a method that uses hand-crafted features to guide learning by explicitly attending to feature indicators when learning the relationship between the input and target variables. In experiments over two different tasks - quality assessment of Wikipedia articles and popularity prediction of online petitions- we demonstrate that the proposed method yields neural models that consistently outperform those that simply use hand-crafted features as side information.
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