Papers with Readability assessment
A Neural Local Coherence Model for Text Quality Assessment (D18-1)
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| Challenge: | Existing approaches to local coherence modeling capture text relatedness at the level of sentence-to-sentence transitions. |
| Approach: | They propose a local coherence model that captures the flow of what connects adjacent sentences . they represent the semantics of a sentence by a vector and capture its state at each word . |
| Outcome: | The proposed model is beneficial for readability assessment and essay scoring tasks. |
A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Loss (2022.emnlp-main)
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| Challenge: | Traditional readability assessment models employ hundreds of linguistic features, but it is less explored for readability assessments. |
| Approach: | They propose a BERT-based model with feature projection and length-balanced loss to determine the difficulty level of a given text. |
| Outcome: | The proposed model achieves significant improvements over baseline models on three English benchmark datasets and one Chinese dataset. |
Trends, Limitations and Open Challenges in Automatic Readability Assessment Research (2022.lrec-1)
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| Challenge: | Readability assessment is the task of evaluating the reading difficulty of a given piece of text. |
| Approach: | They examine the common approaches used for automatic readability assessment and identify their shortcomings and some challenges for the future. |
| Outcome: | The proposed models are compared with existing models and are based on existing ones. |
PromptARA: Improving Deep Representation in Hybrid Automatic Readability Assessment with Prompt and Orthogonal Projection (2023.findings-emnlp)
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| Challenge: | Readability assessment aims to automatically classify texts based on readers’ reading levels. |
| Approach: | They propose a hybrid automatic readability assessment model using prompts to improve deep feature representations and an orthogonal projection layer to fuse both deep and linguistic features. |
| Outcome: | The proposed model outperforms state-of-the-art models over four English and two Chinese corpora and demonstrates that it is more efficient than existing models. |