Cited by 249Topic ModelingText and Document Classification TechnologiesAdvanced Neural Network Applications

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Rationale-Augmented Convolutional Neural Networks for Text Classification

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Zhang, Ye, Marshall, Iain, Wallace, Byron C.·2016·Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

DOI 10.18653/v1/d16-1076PMC PMC5300751

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Abstract

We present a new Convolutional Neural Network (CNN) model for text classification that jointly exploits labels on documents and their constituent sentences.Specifically, we consider scenarios in which annotators explicitly mark sentences (or snippets) that support their overall document categorization, i.e., they provide rationales.Our model exploits such supervision via a hierarchical approach in which each document is represented by a linear combination of the vector representations of its component sentences.We propose a sentence-level convolutional model that estimates the probability that a given sentence is a rationale, and we then scale the contribution of each sentence to the aggregate document representation in proportion to these estimates.Experiments on five classification datasets that have document labels and associated rationales demonstrate that our approach consistently outperforms strong baselines.Moreover, our model naturally provides explanations for its predictions.

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Authors: Zhang, Y., Marshall, I., & Wallace, B. C.
Affiliations: The University of Texas at Austin (US)
King's College London (GB)
Northeastern University (US)
ORCID: Zhang, Ye
Marshall, Iain
Wallace, Byron C.
Venue: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
Publisher: Association for Computational Linguistics
Published: 2016
Volume: 2016
Pages: 795-804
DOI: 10.18653/v1/d16-1076
PMC: PMC5300751
OpenAlex: W2963082289
Cited by: 249
References: 35
Citations by year: 2026: 3 · 2025: 6 · 2024: 18 · 2023: 25 · 2022: 40 · 2021: 46 · 2020: 33 · 2019: 37 · 2018: 22 · 2017: 15 · 2016: 4
FWCI: 14.01
Type: conference paper
Language: en
Open access: hybrid
doi.org
Licence: cc-by
Locations: Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: University of Texas at Austin
National Institutes of Health (5r01lm012086-08)
Medical Research Council (MR/N015185/1)
U.S. National Library of Medicine (R01-LM012086)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: text classification, rationales, explainable classification

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Rationale-Augmented Convolutional Neural Networks for Text Classification

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