Papers by Joël Legrand

5 papers
Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification (2023.emnlp-main)

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Challenge: Existing evaluation metrics conflate simplicity with correlated attributes such as fluency or meaning preservation.
Approach: They propose a new learning evaluation metric that focuses on simplicity outperforming most existing metrics in terms of correlation with human judgements.
Outcome: The proposed metric outperforms most existing metrics in terms of correlation with human judgements.
Discourse-Based Sentence Splitting (2021.findings-emnlp)

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Challenge: Sentence splitting is a key component of sentence simplification and has been shown to help human comprehension.
Approach: They propose to use a discourse connective to generate a sentence that is shorter than the input text.
Outcome: The proposed models outperform end-to-end models in learning the various ways of expressing a discourse relation but generate text that is less grammatical.
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.
Context-Aware Document Simplification (2023.findings-acl)

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Challenge: Recent work on document simplification has focused on sentence-level inputs but fails to preserve the discourse structure.
Approach: They explore various systems that use document context within the simplification process . they investigate the performance and efficiency tradeoffs of system variants .
Outcome: The proposed approach achieves state-of-the-art even when not relying on plan-guidance.
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.
Outcome: The proposed model outperforms baselines on two simplification benchmarks and when used to guide document-level simplification models.

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