Abstract
We propose the novel adaptation of a pre-trained seq2seq model for readability assessment. We prove that a seq2seq model - T5 or BART - can be adapted to discern which text is more difficult from two given texts (pairwise). As an exploratory study to prompt-learn a neural network for text readability in a text-to-text manner, we report useful tips for future work in seq2seq training and ranking-based approach to readability assessment. Specifically, we test nine input-output formats/prefixes and show that they can significantly influence the final model performance.Also, we argue that the combination of text-to-text training and pairwise ranking setup 1) enables leveraging multiple parallel text simplification data for teaching readability and 2) trains a neural model for the general concept of readability (therefore, better cross-domain generalization). At last, we report a 99.6% pairwise classification accuracy on Newsela and a 98.7% for OneStopEnglish, through a joint training approach. Our code is available at github.com/brucewlee/prompt-learning-readability.- Anthology ID:
- 2023.findings-eacl.135
- Volume:
- Findings of the Association for Computational Linguistics: EACL 2023
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1819–1824
- Language:
- URL:
- https://aclanthology.org/2023.findings-eacl.135
- DOI:
- Cite (ACL):
- Bruce W. Lee and Jason Lee. 2023. Prompt-based Learning for Text Readability Assessment. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1819–1824, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- Prompt-based Learning for Text Readability Assessment (Lee & Lee, Findings 2023)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2023.findings-eacl.135.pdf