Papers by Artem Chernodub
Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correction (2022.acl-long)
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| Challenge: | Currently, machine translation (MT) is the mainstream approach for GEC. |
| Approach: | They propose to ensemble Transformer-based encoders by majority votes on span-level edits . their best ensemble achieves a new SOTA result even without pre-training on synthetic datasets - "Troy-Blogs" and "Try-1BW". |
| Outcome: | The proposed model achieves a new SOTA result even without pre-training on synthetic datasets. |
TARGER: Neural Argument Mining at Your Fingertips (P19-3)
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Artem Chernodub, Oleksiy Oliynyk, Philipp Heidenreich, Alexander Bondarenko, Matthias Hagen, Chris Biemann, Alexander Panchenko
| Challenge: | Argumentation is a multi-disciplinary field that extends from philosophy and psychology to linguistics as well as to artificial intelligence. |
| Approach: | They propose to use TARGER to tagging arguments in free text and keyword-based retrieval of arguments from a web-scale corpus. |
| Outcome: | The proposed framework can be used without any reproducibility effort on the user's side and is easily portable to other domains and use cases. |