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MUSS: Multilingual Unsupervised Sentence Simplification by Mining
Paraphrases
Papers citing "MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases"
14 / 14 papers shown
Title |
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 Investigating Large Language Models and Control Mechanisms to Improve
Text Readability of Biomedical AbstractsIEEE International Conference on Healthcare Informatics (ICHI), 2023 |
 Learning to Paraphrase Sentences to Different Complexity LevelsTransactions of the Association for Computational Linguistics (TACL), 2023 |
 Exploiting Summarization Data to Help Text SimplificationConference of the European Chapter of the Association for Computational Linguistics (EACL), 2023 |
 Cognitive Simplification Operations Improve Text SimplificationConference on Computational Natural Language Learning (CoNLL), 2022 |
 Exploiting Social Media Content for Self-Supervised Style TransferInternational Workshop on Natural Language Processing for Social Media (SocialNLP), 2022 |
 Multitasking Framework for Unsupervised Simple Definition GenerationAnnual Meeting of the Association for Computational Linguistics (ACL), 2022 |
 An Unsupervised Method for Building Sentence Simplification Corpora in
Multiple LanguagesConference on Empirical Methods in Natural Language Processing (EMNLP), 2021 |
 Text Simplification by TaggingWorkshop on Innovative Use of NLP for Building Educational Applications (UNBEA), 2021 |
 Billion-scale similarity search with GPUsIEEE Transactions on Big Data (TBD), 2017 |
