| Challenge: | Existing grammatical error correction tools do not provide natural language explanations of errors . a system needs to provide one-sentence explanations for each grammamatical errors in a pair of erroneous and corrected sentences. |
| Approach: | They propose a grammar error explanation task that uses one-sentence explanations for each grammatical error in a pair of erroneous and corrected sentences. |
| Outcome: | The proposed pipeline identifies grammar errors in German, Chinese, and English . human evaluation reveals that 93.9% of German errors, 96.4% of Chinese errors, and 92.20% of English errors are correctly detected and explained. |
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A Simple Recipe for Multilingual Grammatical Error Correction (2021.acl-short)
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Knowledge Editing for Large Language Models (2024.lrec-tutorials)
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Explanation in the Era of Large Language Models (2024.naacl-tutorials)
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| Challenge: | Explanation has long been a part of communication, where humans use language to elucidate each other and transmit information about mechanisms of events. |
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