PromptARA: Improving Deep Representation in Hybrid Automatic Readability Assessment with Prompt and Orthogonal Projection Article Swipe

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Jinshan Zeng , Xianglong Yu , Xianchao Tong , Wenyan Xiao ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.18653/v1/2023.findings-emnlp.1025

Readability assessment aims to automatically classify texts based on readers’ reading levels. The hybrid automatic readability assessment (ARA) models using both deep and linguistic features have attracted rising attention in recent years due to their impressive performance. However, deep features are not fully explored due to the scarcity of training data, and the fusion of deep and linguistic features is not very effective in existing hybrid ARA models. In this paper, we propose a novel hybrid ARA model called PromptARA through employing prompts to improve deep feature representations and an orthogonal projection layer to fuse both deep and linguistic features. A series of experiments are conducted over four English and two Chinese corpora to show the effectiveness of the proposed model. Experimental results demonstrate that the proposed model is superior to state-of-the-art models.

Related Topics

Computer Science

Deep Learning

Artificial Intelligence

Fuse (Electrical)

Reading

Orthographic Projection

Electrical Engineering

Concepts

Readability Computer science Deep learning Artificial intelligence Fuse (electrical) Representation (politics) Natural language processing Feature (linguistics) Projection (relational algebra) Reading (process) Orthographic projection Linguistics Algorithm Engineering Philosophy Law Politics Programming language Electrical engineering Political science

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Type: article
Language: en
Landing Page: https://doi.org/10.18653/v1/2023.findings-emnlp.1025
PDF: https://aclanthology.org/2023.findings-emnlp.1025.pdf
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Cited By: 1
References: 39
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DOI: https://doi.org/10.18653/v1/2023.findings-emnlp.1025

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Title: PromptARA: Improving Deep Representation in Hybrid Automatic Readability Assessment with Prompt and Orthogonal Projection

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Language: en

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Publication year: 2023

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Publication date: 2023-01-01

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Authors: Jinshan Zeng, Xianglong Yu, Xianchao Tong, Wenyan Xiao

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Landing page: https://doi.org/10.18653/v1/2023.findings-emnlp.1025

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Concepts: Readability, Computer science, Deep learning, Artificial intelligence, Fuse (electrical), Representation (politics), Natural language processing, Feature (linguistics), Projection (relational algebra), Reading (process), Orthographic projection, Linguistics, Algorithm, Engineering, Philosophy, Law, Politics, Programming language, Electrical engineering, Political science

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Cited by: 1

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abstract_inverted_index.attention	28
abstract_inverted_index.attracted	26
abstract_inverted_index.automatic	14
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cited_by_percentile_year.max	95
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countries_distinct_count	1
institutions_distinct_count	4
sustainable_development_goals[0].id	https://metadata.un.org/sdg/4
sustainable_development_goals[0].score	0.9100000262260437
sustainable_development_goals[0].display_name	Quality Education
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