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

Daniel Hardt, Dirk Hovy, Sotiris Lamprinidis

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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.
Original languageEnglish
Title of host publicationProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
EditorsEllen Riloff, David Chiang, Julia Hockenmaier, Tsujii Jun’ichi
Number of pages6
Place of PublicationBrussels
PublisherAssociation for Computational Linguistics
Publication date2018
Pages659-664
Publication statusPublished - 2018
Event2018 Conference on Empirical Methods in Natural Language Processing - Square Meeting Center, Brussels, Belgium
Duration: 31 Oct 20184 Nov 2018
http://emnlp2018.org/

Conference

Conference2018 Conference on Empirical Methods in Natural Language Processing
LocationSquare Meeting Center
CountryBelgium
CityBrussels
Period31/10/201804/11/2018
Internet address

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