Hierarchical Interpretation of Neural Text Classification
Recent years have witnessed increasing interest in developing interpretable models in Natural Language Processing (NLP). Most existing models aim at identifying input features such as words or phrases important for model predictions. Neural models developed in NLP, however, often compose word semant...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
The MIT Press
2022-08-01
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| Series: | Computational Linguistics |
| Online Access: | http://dx.doi.org/10.1162/coli_a_00459 |
| _version_ | 1797795394392948736 |
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| author | Hanqi Yan Lin Gui Yulan He |
| author_facet | Hanqi Yan Lin Gui Yulan He |
| author_sort | Hanqi Yan |
| collection | DOAJ |
| description | Recent years have witnessed increasing interest in developing interpretable models in Natural Language Processing (NLP). Most existing models aim at identifying input features such as words or phrases important for model predictions. Neural models developed in NLP, however, often compose word semantics in a hierarchical manner. As such, interpretation by words or phrases only cannot faithfully explain model decisions in text classification. This article proposes a novel Hierarchical Interpretable Neural Text classifier, called HINT, which can automatically generate explanations of model predictions in the form of label-associated topics in a hierarchical manner. Model interpretation is no longer at the word level, but built on topics as the basic semantic unit. Experimental results on both review datasets and news datasets show that our proposed approach achieves text classification results on par with existing
state-of-the-art text classifiers, and generates interpretations more faithful to model predictions and better understood by humans than other interpretable neural text classifiers.1 |
| first_indexed | 2024-03-13T03:18:21Z |
| format | Article |
| id | doaj.art-9b69099fcbd648348b29ec83999dc377 |
| institution | Directory Open Access Journal |
| issn | 1530-9312 |
| language | English |
| last_indexed | 2024-03-13T03:18:21Z |
| publishDate | 2022-08-01 |
| publisher | The MIT Press |
| record_format | Article |
| series | Computational Linguistics |
| spelling | doaj.art-9b69099fcbd648348b29ec83999dc3772023-06-25T14:50:05ZengThe MIT PressComputational Linguistics1530-93122022-08-0148410.1162/coli_a_00459Hierarchical Interpretation of Neural Text ClassificationHanqi YanLin GuiYulan HeRecent years have witnessed increasing interest in developing interpretable models in Natural Language Processing (NLP). Most existing models aim at identifying input features such as words or phrases important for model predictions. Neural models developed in NLP, however, often compose word semantics in a hierarchical manner. As such, interpretation by words or phrases only cannot faithfully explain model decisions in text classification. This article proposes a novel Hierarchical Interpretable Neural Text classifier, called HINT, which can automatically generate explanations of model predictions in the form of label-associated topics in a hierarchical manner. Model interpretation is no longer at the word level, but built on topics as the basic semantic unit. Experimental results on both review datasets and news datasets show that our proposed approach achieves text classification results on par with existing state-of-the-art text classifiers, and generates interpretations more faithful to model predictions and better understood by humans than other interpretable neural text classifiers.1http://dx.doi.org/10.1162/coli_a_00459 |
| spellingShingle | Hanqi Yan Lin Gui Yulan He Hierarchical Interpretation of Neural Text Classification Computational Linguistics |
| title | Hierarchical Interpretation of Neural Text Classification |
| title_full | Hierarchical Interpretation of Neural Text Classification |
| title_fullStr | Hierarchical Interpretation of Neural Text Classification |
| title_full_unstemmed | Hierarchical Interpretation of Neural Text Classification |
| title_short | Hierarchical Interpretation of Neural Text Classification |
| title_sort | hierarchical interpretation of neural text classification |
| url | http://dx.doi.org/10.1162/coli_a_00459 |
| work_keys_str_mv | AT hanqiyan hierarchicalinterpretationofneuraltextclassification AT lingui hierarchicalinterpretationofneuraltextclassification AT yulanhe hierarchicalinterpretationofneuraltextclassification |