| 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. |
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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. |
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. |
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. |
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. |
CWID-hi: A Dataset for Complex Word Identification in Hindi Text (2022.lrec-1)
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| Challenge: | Text simplification is a method for improving the accessibility of text by converting complex sentences into simple sentences. |
| Approach: | They propose to use Hindi knowledge annotators to capture the annotator’s language knowledge to build an automatic complex word classifier using a soft voting approach. |
| Outcome: | The proposed dataset shows that native and non-native annotators perceive complex words differently depending on their language knowledge. |
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. |
A Non-Autoregressive Edit-Based Approach to Controllable Text Simplification (2021.findings-acl)
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| Challenge: | Existing models that generate generic simplified outputs for a given source text have been used to specify output properties. |
| Approach: | They propose a non-autoregressive model that iteratively edits an input sequence and incorporates lexical complexity information into the refinement process to generate simplifications that better match the desired output complexity. |
| Outcome: | The proposed model incorporates lexical complexity information into the refinement process to achieve more complex simplification operations such as content deletion and paraphrasing, as well as sentence splitting. |
Bringing Emerging Architectures to Sequence Labeling in NLP (2026.eacl-long)
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| Challenge: | Pretrained Transformer encoders are the dominant approach to sequence labeling . however, few have been applied to sequence labels on flat or simplified tasks . |
| Approach: | They propose to use pretrained Transformer encoders to model relations across words . they find that the architectures adapt well across tagging tasks that vary in complexity . |
| Outcome: | The proposed architectures perform well across tagging tasks across languages and datasets. |
Detecting Multiword Expression Type Helps Lexical Complexity Assessment (2020.lrec-1)
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| Challenge: | Multiword expressions (MWEs) represent lexemes that should be treated as single lexical units due to their idiosyncratic nature. |
| Approach: | They re-annotate a complex word identification shared task 2018 dataset . they find that a lexical complexity assessment system benefits from the information . |
| Outcome: | The proposed dataset provides valuable information for the text simplification community. |