Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning

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Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning

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Title: Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning
Author: Hardt, Daniel; Hovy, Dirk; Sotiris, Lamprinidis
Abstract: Newspapers need to attract readers with headlines, anticipating their readers’ preferences. These preferences rely on topical, structural, and lexical factors. We model each of these factors in a multi-task GRU network to predict headline popularity. We find that pre-trained word embeddings provide significant improvements over untrained embeddings, as do the combination of two auxiliary tasks, newssection prediction and part-of-speech tagging. However, we also find that performance is very similar to that of a simple Logistic Regression model over character n-grams. Feature analysis reveals structural patterns of headline popularity, including the use of forward-looking deictic expressions and second person pronouns.
URI: http://hdl.handle.net/10398/9683
Date: 2018-11-08
Notes: Paper presented at the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP2018). October 31 - November 4 2018, Brussels, Belgium

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