DeepAI AI Chat
Log In Sign Up

Knowledge-Rich BERT Embeddings for Readability Assessment

by   Joseph Marvin Imperial, et al.

Automatic readability assessment (ARA) is the task of evaluating the level of ease or difficulty of text documents for a target audience. For researchers, one of the many open problems in the field is to make such models trained for the task show efficacy even for low-resource languages. In this study, we propose an alternative way of utilizing the information-rich embeddings of BERT models through a joint-learning method combined with handcrafted linguistic features for readability assessment. Results show that the proposed method outperforms classical approaches in readability assessment using English and Filipino datasets, and obtaining as high as 12.4 We also show that the knowledge encoded in BERT embeddings can be used as a substitute feature set for low-resource languages like Filipino with limited semantic and syntactic NLP tools to explicitly extract feature values for the task.


page 1

page 2

page 3

page 4


Automatic Readability Assessment for Closely Related Languages

In recent years, the main focus of research on automatic readability ass...

A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Loss

For readability assessment, traditional methods mainly employ machine le...

Under the Microscope: Interpreting Readability Assessment Models for Filipino

Readability assessment is the process of identifying the level of ease o...

KinyaBERT: a Morphology-aware Kinyarwanda Language Model

Pre-trained language models such as BERT have been successful at tacklin...

Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment

Deep learning models for automatic readability assessment generally disc...

Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages

Evaluating model robustness is critical when developing trustworthy mode...