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ACL Findings2024

Text Simplification via Adaptive Teaching

Seyed Ali Bahrainian, Jonathan Dou, Carsten Eickhoff

Abstract

Text simplification is the process of rewriting a piece of text using simpler vocabulary and grammatical structure in order to make the text more accessible and understandable for a larger audience.

In this paper, we introduce a new text simplification model based on the notion of adaptive teaching using a teacher network and a text generation network. We name this new model Simplification via Adaptive Teaching (SAT). Our proposed model sets a new state-of-the-art performance in terms of standard simplification metrics such as SARI and D-SARI with a significant improvement over the previous state of the art on the D-Wikipedia dataset and the Wiki-Doc benchmark dataset. Moreover, we conduct a human evaluation in terms of text simplicity, correctness, and fluency to substantiate SAT's performance.

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Cited by top-tier papers2

  1. Interpretability Analysis of Arithmetic In-Context Learning in Large Language ModelsGregory Polyakov, Christian Hepting, Carsten Eickhoff, Seyed Ali BahrainianEMNLP 2025 · 1 citation
  2. PersonalityDBench: A Dataset for Personality Disorders - from Modeling to Controlled GenerationFederico Ravenda, Seyed Ali Bahrainian, Daniele Montagnani, Antonietta Mira et al.ACL 2026

Builds on8

  1. MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence FrontiersKrishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun et al.NeurIPS 2021 · 606 citations
  2. BRIO: Bringing Order to Abstractive SummarizationYixin Liu, Pengfei Liu, Dragomir R. Radev, Graham NeubigACL 2022 · 329 citations
  3. Document-Level Text Simplification: Dataset, Criteria and BaselineRenliang Sun, Hanqi Jin, Xiaojun WanEMNLP 2021 · 33 citations
  4. NEWTS: A Corpus for News Topic-Focused SummarizationSeyed Ali Bahrainian, Sheridan Feucht, Carsten EickhoffACL Findings 2022 · 15 citations
  5. SIMSUM: Document-level Text Simplification via Simultaneous SummarizationSofia Blinova, Xinyu Zhou, Martin Jaggi, Carsten Eickhoff et al.ACL 2023 · 11 citations

Related papers

  1. Towards Reference-free Text Simplification Evaluation with a BERT Siamese Network ArchitectureXinran Zhao, Esin Durmus, Dit-Yan YeungACL Findings 2023 · 5 citations
  2. Controlling Pre-trained Language Models for Grade-Specific Text SimplificationSweta Agrawal, Marine CarpuatEMNLP 2023 · 5 citations
  3. Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising AutoencodersYanbin Zhao, Lu Chen, Zhi Chen, Kai YuAAAI 2020 · 39 citations
  4. Keep It Simple: Unsupervised Simplification of Multi-Paragraph TextPhilippe Laban, Tobias Schnabel, Paul N. Bennett, Marti A. HearstACL 2021
  5. Explainable Prediction of Text Complexity: The Missing Preliminaries for Text SimplificationCristina Garbacea, Mengtian Guo, Samuel Carton, Qiaozhu MeiACL 2021
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