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Is your document novel? Let attention guide you. An attention-based model for document-level novelty detection

Published online by Cambridge University Press:  24 April 2020

Tirthankar Ghosal ,
Vignesh Edithal ,
Asif Ekbal ,
Pushpak Bhattacharyya ,
Srinivasa Satya Sameer Kumar Chivukula  and
George Tsatsaronis
Tirthankar Ghosal*
Affiliation:
Indian Institute of Technology Patna, Bihta, Bihar, Patna801103, India
Vignesh Edithal
Affiliation:
Indian Institute of Technology Patna, Bihta, Bihar, Patna801103, India
Asif Ekbal
Affiliation:
Indian Institute of Technology Patna, Bihta, Bihar, Patna801103, India
Pushpak Bhattacharyya
Affiliation:
Indian Institute of Technology Patna, Bihta, Bihar, Patna801103, India
Srinivasa Satya Sameer Kumar Chivukula
Affiliation:
Elsevier, Amsterdam, Netherlands
George Tsatsaronis
Affiliation:
Elsevier, Amsterdam, Netherlands
*
*Corresponding author. E-mail: tirthankar.slg@gmail.com
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Abstract

Detecting, whether a document contains sufficient new information to be deemed as novel, is of immense significance in this age of data duplication. Existing techniques for document-level novelty detection mostly perform at the lexical level and are unable to address the semantic-level redundancy. These techniques usually rely on handcrafted features extracted from the documents in a rule-based or traditional feature-based machine learning setup. Here, we present an effective approach based on neural attention mechanism to detect document-level novelty without any manual feature engineering. We contend that the simple alignment of texts between the source and target document(s) could identify the state of novelty of a target document. Our deep neural architecture elicits inference knowledge from a large-scale natural language inference dataset, which proves crucial to the novelty detection task. Our approach is effective and outperforms the standard baselines and recent work on document-level novelty detection by a margin of $\sim$ 3% in terms of accuracy.

Keywords

Document-Level Novelty DetectionDecomposable AttentionNatural Language InferenceDocument Classification
Type
Article
Information
Natural Language Engineering , Volume 27 , Issue 4 , July 2021 , pp. 427 - 454
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Copyright
© Cambridge University Press 2020

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