Papers with CWI
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. |
Simplification Using Paraphrases and Context-Based Lexical Substitution (N18-1)
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| Challenge: | Lexical simplification involves identifying complex words or phrases that need to be simplified and suggesting simpler meaning-preserving substitutes. |
| Approach: | They propose a complex word identification model that exploits both lexical and contextual features and a word-embedding lexical substitution model to replace the detected complex words with simpler paraphrases. |
| Outcome: | The proposed model detects complex words with higher accuracy than other models and proposes good substitutes in context. |
Personalizing Lexical Simplification (C18-1)
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| Challenge: | Experimental results show that even a simple personalized CWI model can help the system avoid some unnecessary simplifications and produce more readable output. |
| Approach: | They evaluate the performance of a state-of-the-art LS system on individual learners of English at different proficiency levels and measure the benefits of using complex word identification models to personalize the system. |
| Outcome: | The proposed system produces a more readable output for learners with special needs and those with language disabilities. |
One Size Does Not Fit All: The Case for Personalised Word Complexity Models (2022.findings-naacl)
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| Challenge: | Complex word identification (CWI) aims to identify words in a text that are difficult for a reader to understand and therefore benefit from simplification. |
| Approach: | They propose to use a novel active learning framework to tailor models to individual readers and release a dataset of complexity annotations and models as a benchmark for further research. |
| Outcome: | The proposed model can be tailored to individual readers and released as a benchmark for future research. |
Strong Baselines for Complex Word Identification across Multiple Languages (N19-1)
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Pierre Finnimore, Elisabeth Fritzsch, Daniel King, Alison Sneyd, Aneeq Ur Rehman, Fernando Alva-Manchego, Andreas Vlachos
| Challenge: | Complex Word Identification (CWI) is the task of identifying which words or phrases in a sentence are difficult to understand by a specific type of reader. |
| Approach: | They propose to use monolingual and cross-lingual CWI models to make predictions for languages not seen during training. |
| Outcome: | The proposed models perform as well as (or better than) most models submitted to the latest CWI Shared Task. |
Complex Word Identification as a Sequence Labelling Task (P19-1)
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| Challenge: | Complex Word Identification (CWI) is a crucial first step in a simplification pipeline. |
| Approach: | They propose a system that performs CWI in context without extensive feature engineering and outperforms state-of-the-art systems on this task. |
| Outcome: | The proposed system outperforms state-of-the-art systems on complex word identification. |
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. |