# Auto-Select Reading Passages in English Assessment Tests?

arXiv: https://arxiv.org/abs/2205.06961 · HTML: https://mlsanity.com/paper/2205.06961/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 14 May 2022.

## Builds on (most cited first, 4 of 4)

- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Text Readability Assessment for Second Language Learners](https://mlsanity.com/paper/1906.07580/) (2019)
- [Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features](https://mlsanity.com/paper/2109.12258/) (2021)
- [LXPER Index 2.0: Improving Text Readability Assessment Model for L2 English Students in Korea](https://mlsanity.com/paper/2010.13374/) (2020)

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

- [LFTK: Handcrafted Features in Computational Linguistics](https://mlsanity.com/paper/2305.15878/) (2023)

## Abstract (arXiv)

We show a method to auto-select reading passages in English assessment tests and share some key insights that can be helpful in related fields. In specifics, we prove that finding a similar passage (to a passage that already appeared in the test) can give a suitable passage for test development. In the process, we create a simple database-tagger-filter algorithm and perform a human evaluation. However, 1. the textual features, that we analyzed, lack coverage, and 2. we fail to find meaningful correlations between each feature and suitability score. Lastly, we describe the future developments to improve automated reading passage selection.