Papers by Jonathan Chevelu
Mama/Papa, Is this Text for Me? (2020.coling-main)
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Rashedur Rahman, Gwénolé Lecorvé, Aline Étienne, Delphine Battistelli, Nicolas Béchet, Jonathan Chevelu
| Challenge: | Existing methods to predict minimal age from which text can be understood for children are unresolved in computational linguistics. |
| Approach: | They propose a method which predicts the minimum age from which a text can be understood by a recurrent neural network. |
| Outcome: | The proposed method outperforms state-of-the-art models at sentence and text levels and achieves mean absolute errors of 1.86 and 2.28. |
Paraphrase Generation Evaluation Powered by an LLM: A Semantic Metric, Not a Lexical One (2025.coling-main)
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| Challenge: | Existing measures for automatic paraphrase generation are based on lexical distances or semantic embedding alignments. |
| Approach: | They propose a measure based on a log likelihood ratio from an LLM to assess the quality of a potential paraphrase. |
| Outcome: | The proposed measure is better for sorting pairs of sentences by semantic proximity and provides an interpretable classification threshold between paraphrases and non-paraphrases. |
Neural-Driven Search-Based Paraphrase Generation (2021.eacl-main)
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| Challenge: | Existing non-supervised paraphrase generation models are biased toward specific problems like question answering or image captioning. |
| Approach: | They propose a search-based paraphrase generation scheme where candidate paraphrases are generated by iterated transformations from the original sentence and evaluated in terms of syntax quality, semantic distance, and lexical distance. |
| Outcome: | The proposed algorithms perform well against non-supervised baselines. |