InterpretARA: Enhancing Hybrid Automatic Readability Assessment with Linguistic Feature Interpreter and Contrastive Learning Article Swipe
Jinshan Zeng
,
Xianchao Tong
,
Xianglong Yu
,
Wenyan Xiao
,
Qing Huang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v38i17.29921
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v38i17.29921
The hybrid automatic readability assessment (ARA) models that combine deep and linguistic features have recently received rising attention due to their impressive performance. However, the utilization of linguistic features is not fully realized, as ARA models frequently concentrate excessively on numerical values of these features, neglecting valuable structural information embedded within them. This leads to limited contribution of linguistic features in these hybrid ARA models, and in some cases, it may even result in counterproductive outcomes. In this paper, we propose a novel hybrid ARA model named InterpretARA through introducing a linguistic interpreter to better comprehend the structural information contained in linguistic features, and leveraging the contrastive learning that enables the model to understand relative difficulty relationships among texts and thus enhances deep representations. Both document-level and segment-level deep representations are extracted and used for the readability assessment. A series of experiments are conducted over four English corpora and one Chinese corpus to demonstrate the effectiveness of the proposed model. Experimental results show that InterpretARA outperforms state-of-the-art models in most corpora, and the introduced linguistic interpreter can provide more useful information than existing ways for ARA.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v38i17.29921
- https://ojs.aaai.org/index.php/AAAI/article/download/29921/31611
- OA Status
- diamond
- Cited By
- 1
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393160967
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4393160967Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v38i17.29921Digital Object Identifier
- Title
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InterpretARA: Enhancing Hybrid Automatic Readability Assessment with Linguistic Feature Interpreter and Contrastive LearningWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-24Full publication date if available
- Authors
-
Jinshan Zeng, Xianchao Tong, Xianglong Yu, Wenyan Xiao, Qing HuangList of authors in order
- Landing page
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https://doi.org/10.1609/aaai.v38i17.29921Publisher landing page
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https://ojs.aaai.org/index.php/AAAI/article/download/29921/31611Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://ojs.aaai.org/index.php/AAAI/article/download/29921/31611Direct OA link when available
- Concepts
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Interpreter, Readability, Linguistics, Feature (linguistics), Computer science, Natural language processing, Artificial intelligence, Psychology, Programming language, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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26Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.model. | 159 |
| abstract_inverted_index.models | 6, 35, 167 |
| abstract_inverted_index.paper, | 78 |
| abstract_inverted_index.result | 72 |
| abstract_inverted_index.rising | 16 |
| abstract_inverted_index.series | 139 |
| abstract_inverted_index.useful | 179 |
| abstract_inverted_index.values | 41 |
| abstract_inverted_index.within | 50 |
| abstract_inverted_index.Chinese | 150 |
| abstract_inverted_index.English | 146 |
| abstract_inverted_index.combine | 8 |
| abstract_inverted_index.corpora | 147 |
| abstract_inverted_index.enables | 109 |
| abstract_inverted_index.limited | 55 |
| abstract_inverted_index.models, | 64 |
| abstract_inverted_index.propose | 80 |
| abstract_inverted_index.provide | 177 |
| abstract_inverted_index.results | 161 |
| abstract_inverted_index.through | 88 |
| abstract_inverted_index.However, | 23 |
| abstract_inverted_index.corpora, | 170 |
| abstract_inverted_index.embedded | 49 |
| abstract_inverted_index.enhances | 121 |
| abstract_inverted_index.existing | 182 |
| abstract_inverted_index.features | 12, 28, 59 |
| abstract_inverted_index.learning | 107 |
| abstract_inverted_index.proposed | 158 |
| abstract_inverted_index.received | 15 |
| abstract_inverted_index.recently | 14 |
| abstract_inverted_index.relative | 114 |
| abstract_inverted_index.valuable | 46 |
| abstract_inverted_index.attention | 17 |
| abstract_inverted_index.automatic | 2 |
| abstract_inverted_index.conducted | 143 |
| abstract_inverted_index.contained | 99 |
| abstract_inverted_index.extracted | 131 |
| abstract_inverted_index.features, | 44, 102 |
| abstract_inverted_index.numerical | 40 |
| abstract_inverted_index.outcomes. | 75 |
| abstract_inverted_index.realized, | 32 |
| abstract_inverted_index.assessment | 4 |
| abstract_inverted_index.comprehend | 95 |
| abstract_inverted_index.difficulty | 115 |
| abstract_inverted_index.frequently | 36 |
| abstract_inverted_index.impressive | 21 |
| abstract_inverted_index.introduced | 173 |
| abstract_inverted_index.leveraging | 104 |
| abstract_inverted_index.linguistic | 11, 27, 58, 91, 101, 174 |
| abstract_inverted_index.neglecting | 45 |
| abstract_inverted_index.structural | 47, 97 |
| abstract_inverted_index.understand | 113 |
| abstract_inverted_index.assessment. | 137 |
| abstract_inverted_index.concentrate | 37 |
| abstract_inverted_index.contrastive | 106 |
| abstract_inverted_index.demonstrate | 153 |
| abstract_inverted_index.excessively | 38 |
| abstract_inverted_index.experiments | 141 |
| abstract_inverted_index.information | 48, 98, 180 |
| abstract_inverted_index.interpreter | 92, 175 |
| abstract_inverted_index.introducing | 89 |
| abstract_inverted_index.outperforms | 165 |
| abstract_inverted_index.readability | 3, 136 |
| abstract_inverted_index.utilization | 25 |
| abstract_inverted_index.Experimental | 160 |
| abstract_inverted_index.InterpretARA | 87, 164 |
| abstract_inverted_index.contribution | 56 |
| abstract_inverted_index.performance. | 22 |
| abstract_inverted_index.effectiveness | 155 |
| abstract_inverted_index.relationships | 116 |
| abstract_inverted_index.segment-level | 127 |
| abstract_inverted_index.document-level | 125 |
| abstract_inverted_index.representations | 129 |
| abstract_inverted_index.representations. | 123 |
| abstract_inverted_index.state-of-the-art | 166 |
| abstract_inverted_index.counterproductive | 74 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.36004217 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |