# A Simple Post-Processing Technique for Improving Readability Assessment of Texts using Word Mover's Distance

arXiv: https://arxiv.org/abs/2103.07277 · HTML: https://mlsanity.com/paper/2103.07277/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 12 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Text Readability Assessment for Second Language Learners](https://mlsanity.com/paper/1906.07580/) (2019)
- [Supervised and Unsupervised Neural Approaches to Text Readability](https://mlsanity.com/paper/1907.11779/) (2019)
- [Linguistic Features for Readability Assessment](https://mlsanity.com/paper/2006.00377/) (2020)
- [ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis](https://mlsanity.com/paper/2103.04083/) (2021)
- [Application of Lexical Features Towards Improvement of Filipino Readability Identification of Children's Literature](https://mlsanity.com/paper/2101.10537/) (2021)

## Built on by (most cited first, 1 of 1)

- [BERT Embeddings for Automatic Readability Assessment](https://mlsanity.com/paper/2106.07935/) (2021)

## Abstract (arXiv)

Assessing the proper difficulty levels of reading materials or texts in general is the first step towards effective comprehension and learning. In this study, we improve the conventional methodology of automatic readability assessment by incorporating the Word Mover's Distance (WMD) of ranked texts as an additional post-processing technique to further ground the difficulty level given by a model. Results of our experiments on three multilingual datasets in Filipino, German, and English show that the post-processing technique outperforms previous vanilla and ranking-based models using SVM.