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

Jinshan Zeng, Xianglong Yu, Xianchao Tong, Wenyan Xiao

Submission Type: Regular Long Paper

Submission Track: Machine Learning for NLP

Submission Track 2: NLP Applications

Keywords: Readability assessment; Deep learning; Prompt learning; Orthogonal projection layer; Linguistic feature

TL;DR: This paper proposes a novel model called PromptARA for the automatic readability assessment through employing prompts and an orthogonal projection layer to improve deep feature representations.

Abstract: 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.

Submission Number: 2244