| Challenge: | Existing models that only consider the sentence level generate words beyond the target level. |
| Approach: | They propose a method to control the level of a sentence in a text simplification task . they add the target grade level as input and weight words in the loss function . |
| Outcome: | The proposed method improves both BLEU and SARI scores and achieves aggressive rewriting. |
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Controlling Pre-trained Language Models for Grade-Specific Text Simplification (2023.emnlp-main)
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| Challenge: | Existing approaches to text simplification control output complexity at corpus level disregarding complexity of individual inputs and considering only one level of output complexity. |
| Approach: | They propose a method that predicts edit operations required for a specific grade level . they say this approach improves the quality of the simplified outputs over corpus-level heuristics . |
| Outcome: | The proposed method improves the readability of simplified outputs over corpus-level search-based heuristics. |
Controllable Text Simplification with Deep Reinforcement Learning (2022.aacl-short)
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| Challenge: | Existing methods for controlling sentence difficulty have not taken into account sentence-level difficulties. |
| Approach: | They propose a method for controlling the difficulty of a sentence based on deep reinforcement learning. |
| Outcome: | The proposed method generates sentences of appropriate difficulty for the target audience through reinforcement learning. |
A Non-Autoregressive Edit-Based Approach to Controllable Text Simplification (2021.findings-acl)
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| Challenge: | Existing models that generate generic simplified outputs for a given source text have been used to specify output properties. |
| Approach: | They propose a non-autoregressive model that iteratively edits an input sequence and incorporates lexical complexity information into the refinement process to generate simplifications that better match the desired output complexity. |
| Outcome: | The proposed model incorporates lexical complexity information into the refinement process to achieve more complex simplification operations such as content deletion and paraphrasing, as well as sentence splitting. |
Document-Level Planning for Text Simplification (2023.eacl-main)
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| Challenge: | Existing work on text simplification is limited to sentence-level inputs . attempts to iteratively apply these approaches fail to preserve discourse structure of document . |
| Approach: | They propose a simplification plan that labels each sentence in the input document while considering both its context and internal structure. |
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Edit-Constrained Decoding for Sentence Simplification (2024.findings-emnlp)
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| Challenge: | Existing studies have shown that lexically constrained decoding is effective for sentence simplification, but their constraints can be loose and may lead to sub-optimal generation. |
| Approach: | They propose an edit operation based on lexically constrained decoding for sentence simplification using a dictionary of technical terms as constraints. |
| Outcome: | The proposed method outperforms previous studies on English simplification corpora and is based on lexical paraphrasing. |
Controllable Sentence Simplification via Operation Classification (2022.findings-naacl)
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| Challenge: | Sentence simplification involves a sentence being transformed into a simpler version of itself while preserving its core meaning. |
| Approach: | They propose a controllable-simplification model that tailors simplifications to four global operations . they propose to use a dataset to train highly accurate classification systems for these operations based on syntactic or discourse structure . |
| Outcome: | The proposed model outperforms both end-to-end and controllable approaches in sentence simplification tasks. |
Controllable Sentence Simplification (2020.lrec-1)
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| Challenge: | Text simplification is often considered an all-purpose generic task where the same simplifications are suitable for all but multiple audiences can benefit from simplified text in different ways. |
| Approach: | They propose a controllable simplification model that provides explicit control on simplification systems based on Sequence-to-Sequence models. |
| Outcome: | The proposed model outperforms standard models on simplification benchmarks. |
Learning Simplifications for Specific Target Audiences (P18-2)
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| Challenge: | Text simplification is a monolingual text-to-text transformation task . data from TS data can contain multiple simplifications of the same original text . |
| Approach: | They propose to use sequence-to-sequence neural models to build models tailored for specific grade levels. |
| Outcome: | The proposed model outperforms state-of-the-art approaches for a monolingual text-to-text transformation task. |
Adapting Sentence-level Automatic Metrics for Document-level Simplification Evaluation (2025.naacl-long)
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| Challenge: | Existing studies on text simplification have focused on sentence simplification, but these metrics often underperform on longer texts. |
| Approach: | They propose to adapt existing sentence-level metrics for paragraph- or document-level simplification by incorporating a new approach to the evaluation of text simplification metrics. |
| Outcome: | The proposed approach outperforms existing sentence-level metrics in terms of correlation with human judgment and the sensitivity and robustness of various metrics to different types of errors produced by existing systems. |
Aligning Sentence Simplification with ESL Learner’s Proficiency for Language Acquisition (2025.naacl-long)
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| Challenge: | Text simplification is crucial for improving accessibility and comprehension for English as a Second Language (ESL) learners. |
| Approach: | They propose to simplify complex sentences to appropriate levels while also increasing vocabulary coverage of the target level. |
| Outcome: | The proposed method can increase frequency and diversity of vocabulary of the target level by more than 20% compared to baseline models, while maintaining high simplification quality. |