Academic Journal
InterpretARA: Enhancing Hybrid Automatic Readability Assessment with Linguistic Feature Interpreter and Contrastive Learning
| العنوان: | InterpretARA: Enhancing Hybrid Automatic Readability Assessment with Linguistic Feature Interpreter and Contrastive Learning |
|---|---|
| المؤلفون: | Zeng, Jinshan, Tong, Xianchao, Yu, Xianglong, Xiao, Wenyan, Huang, Qing |
| المصدر: | Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 38 No. 17: AAAI-24 Technical Tracks 17; 19497-19505 ; 2374-3468 ; 2159-5399 |
| بيانات النشر: | Association for the Advancement of Artificial Intelligence |
| سنة النشر: | 2024 |
| المجموعة: | Association for the Advancement of Artificial Intelligence: AAAI Publications |
| مصطلحات موضوعية: | NLP: Applications, ML: Classification and Regression, NLP: Interpretability, Analysis, and Evaluation of NLP Models, NLP: Machine Translation, Multilinguality, Cross-Lingual NLP, NLP: Sentiment Analysis, Stylistic Analysis, and Argument Mining, NLP: Text Classification |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| Relation: | https://ojs.aaai.org/index.php/AAAI/article/view/29921/31611; https://ojs.aaai.org/index.php/AAAI/article/view/29921 |
| DOI: | 10.1609/aaai.v38i17.29921 |
| الاتاحة: | https://ojs.aaai.org/index.php/AAAI/article/view/29921 https://doi.org/10.1609/aaai.v38i17.29921 |
| Rights: | Copyright (c) 2024 Association for the Advancement of Artificial Intelligence |
| رقم الانضمام: | edsbas.73D12AB4 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1609/aaai.v38i17.29921 |
|---|