Papers with English learners
Controlling Grammatical Error Correction Using Word Edit Rate (P19-2)
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| Challenge: | Existing models for grammatical error correction only consider the single degree of correction suited for training corpus. |
| Approach: | They propose a neural grammar error correction method that can control the degree of correction by using new training data annotated with word edit rate. |
| Outcome: | The proposed method improves correction accuracy by using training data annotated with word edit rate. |
Level-Up: Learning to Improve Proficiency Level of Essays (P19-3)
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| Challenge: | Many essays are submitted to tutoring services by English learners on the Web every day . few systems provide focused suggestions on how to raise the level of proficiency. |
| Approach: | They propose a method for generating suggestions on a sentence for improving proficiency . they propose identifying grammatical elements and ranking related elements to provide suggestions . |
| Outcome: | The proposed method helps english learners improve their writing and reading skills. |
ELQA: A Corpus of Metalinguistic Questions and Answers about English (2023.acl-long)
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| Challenge: | ELQA corpus is metalinguistic—it consists of language about language. |
| Approach: | They present a corpus of questions and answers in and about the English language . they use a free-form question answering task and multiple LLMs to analyze their capacity . |
| Outcome: | The ELQA corpus covers grammar, meaning, fluency, and etymology . the results can be used to investigate metalinguistic capabilities of NLU models . |
Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency (2024.lrec-main)
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| Challenge: | generative LLMs have been known for overcorrection where results obtain higher recall measures than precision measures. |
| Approach: | They propose to use generative LLMs to prompt grammatical error correction using a model based on language proficiency to examine the interaction between LLM's performance and L2 language proficiency. |
| Outcome: | The proposed model improves on zero-shot and few-shot prompting and fine-tuning models for grammatical error correction for learners of English as a foreign language based on the different proficiency levels. |
Smart Word Suggestions for Writing Assistance (2023.findings-acl)
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| Challenge: | Using word suggestions, writing assistance is a widely used application of natural language processing (NLP) . a task is performed to identify words or phrases that require improvement and provide substitution suggestions for each improvable target. |
| Approach: | They propose a task and benchmark to help writers improve word usage . they use human-labeled data and a distantly supervised dataset for testing . |
| Outcome: | The proposed task and benchmark aims to improve word usage in writing aids. |