Rationale-Augmented Convolutional Neural Networks for Text Classification
Open accessAbstract
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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The prior work predicted whether studies' results might be at risk of bias using the first version of the Cochrane Risk of Bias tool.
“In this prior work, we predicted whether studies’ results might be at risk of bias using the first version of the Cochrane Risk of Bias tool”
From Trialstreamer: A living, automatically updated database of clinical trial reports (2020) · cites this paper as [15]
Section 5.1 (page unavailable) says the paper used risk-of-bias classification datasets for randomized trials and assessed four domains defined by the Cochrane Risk of Bias Tool. Section 7.2 gives examples of studies classified as at low or high risk of bias. The copy does not identify the tool as its first version, so that detail is not verified. (LLM, conf=0.83)
Experimental — accuracy not yet measuredData extraction systems have been created that can automatically retrieve unstructured information on the trial PICO elements and risk of bias.
“Data extraction systems have been created that can automatically retrieve unstructured information on the trial PICO elements,10–12 and risk of bias.13–16”
From Trialstreamer: A living, automatically updated database of clinical trial reports (2020) · cites this paper as [15]
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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)
- Venue
- Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
- Published
- 2016
- Volume
- 2016
- Pages
- 795-804
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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