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        <title>Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning</title>
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        <namePart type="given">Sotiris</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
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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, news-section 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.</abstract>
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%0 Conference Proceedings
%T Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning
%A Lamprinidis, Sotiris
%A Hardt, Daniel
%A Hovy, Dirk
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F lamprinidis-etal-2018-predicting
%X 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, news-section 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.
%R 10.18653/v1/D18-1068
%U https://aclanthology.org/D18-1068
%U https://doi.org/10.18653/v1/D18-1068
%P 659-664

Markdown (Informal)

[Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning](https://aclanthology.org/D18-1068) (Lamprinidis et al., EMNLP 2018)

• Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning (Lamprinidis et al., EMNLP 2018)

ACL

• Sotiris Lamprinidis, Daniel Hardt, and Dirk Hovy. 2018. Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 659–664, Brussels, Belgium. Association for Computational Linguistics.