Papers by Elizaveta Korotkova
No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models (2024.eacl-long)
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| Challenge: | Grammatical Error Correction (GEC) research has primarily focused on English with little coverage for other languages. |
| Approach: | They propose a multilingual machine translation model that can be fine-tuned to improve error correction out-of-the-box. |
| Outcome: | The proposed model outperforms similar-sized MT5 models and competes favourably with larger models. |
Multilinguality or Back-translation? A Case Study with Estonian (2024.lrec-main)
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| Challenge: | a limited amount of parallel data is available for machine translation, and synthetic data is often used to improve translation quality. |
| Approach: | They propose a large-scale synthetic corpus of Estonian translations that contains over 1 billion parallel sentences. |
| Outcome: | The proposed model improves the baseline model while maintaining multilinguality . the proposed model is 6 times larger than the Estonian corpus and twice the size of the Estonial part of the CulturaX corpus. |
BPE Gets Picky: Efficient Vocabulary Refinement During Tokenizer Training (2024.emnlp-main)
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| Challenge: | Tokenization is a relatively understudied area, but it can greatly impact model performance and efficiency. |
| Approach: | They propose a modified BPE tokenizer that removes merges that leave intermediate "junk" tokens from the vocabulary. |
| Outcome: | The proposed method improves vocabulary efficiency, eliminates under-trained tokens, and does not compromise text compression. |