Papers with lexical complexity prediction
Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification (2022.acl-long)
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| Challenge: | Existing datasets for complex word identification (CWI) are limited and the difficulty of the task is augmented by the scarcity of input examples. |
| Approach: | They propose a novel training technique for the complex word identification task based on domain adaptation to improve character and context representations. |
| Outcome: | The proposed training technique improves the target character and context representations and also smooths differences between datasets. |
Complex Word Identification: A Comparative Study between ChatGPT and a Dedicated Model for This Task (2024.lrec-main)
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| Challenge: | Existing methods to assess lexical complexity are used to evaluate the difficulty of vocabulary for language learners. |
| Approach: | They propose to use pre-trained language models to assess the complexity of a word based on its context. |
| Outcome: | The proposed method outperforms the best systems in SemEval-2021. |
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups (2024.emnlp-main)
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Răzvan-Alexandru Smădu, David-Gabriel Ion, Dumitru-Clementin Cercel, Florin Pop, Mihaela-Claudia Cercel
| Challenge: | Large language models (LLMs) are popular in the Natural Language Processing community because of their versatility and capability to solve unseen tasks in zero/few-shot settings. |
| Approach: | They investigate the use of large language models in CWI, LCP, and MWE settings by evaluating their use in zero-shot, few-shot and fine-tuning settings. |
| Outcome: | The proposed models struggle in certain conditions or achieve comparable results against existing methods. |
RALS: Resources and Baselines for Romanian Automatic Lexical Simplification (2025.emnlp-main)
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| Challenge: | Text simplification is the process of transforming texts into variants that are simpler to understand by larger audiences or easier to process by existing NLP systems. |
| Approach: | They propose a method for ordering simplification suggestions using a pairwise ranking approximation method, arranging candidates from simple to complex based on a separate set of human judgments. |
| Outcome: | The proposed system is the first to combine lexical simplification and complexity prediction in Romanian with human lexicals. |