Try to resolve the strange file diff by resetting branch to master (s…

…ebastianruder#615)

english/grammatical_error_correction.md

@@ -18,10 +18,13 @@ The shared task setting restricts that systems use only publicly available datas
 
 | Model           | F0.5  |  Paper / Source | Code |
 | ------------- | :-----:| --- | :-----: |
+| ESC (Qorib et al., NAACL 2022) | 69.51 | [Frustratingly Easy System Combination for Grammatical Error Correction](https://aclanthology.org/2022.naacl-main.143/) | [official](https://github.com/nusnlp/esc) |
 | T5 ([t5.1.1.xxl](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/released_checkpoints.md)) trained on [cLang-8](https://github.com/google-research-datasets/clang8) (Rothe et al., ACL-IJCNLP 2021) | 68.87 | [A Simple Recipe for Multilingual Grammatical Error Correction](https://arxiv.org/pdf/2106.03830.pdf) | [T5](https://github.com/google-research/text-to-text-transfer-transformer), [cLang-8](https://github.com/google-research-datasets/clang8) |
 | Tagged corruptions - ensemble (Stahlberg and Kumar, 2021)| 68.3 | [Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Models](https://www.aclweb.org/anthology/2021.bea-1.4.pdf)| [Official](https://github.com/google-research-datasets/C4_200M-synthetic-dataset-for-grammatical-error-correction) |
+| TMTC (Lai et al., ACL Findings 2022) | 67.02 | [Type-Driven Multi-Turn Corrections for Grammatical Error Correction](https://aclanthology.org/2022.findings-acl.254) | [official](https://github.com/DeepLearnXMU/TMTC) |
 | Sequence tagging + token-level transformations + two-stage fine-tuning + (BERT, RoBERTa, XLNet), ensemble (Omelianchuk et al., BEA 2020) | 66.5 | [GECToR – Grammatical Error Correction: Tag, Not Rewrite](https://arxiv.org/pdf/2005.12592.pdf) | [Official](https://github.com/grammarly/gector) |
 | Shallow Aggressive Decoding with BART (12+2), single model (beam=1) (Sun et al., ACL 2021) | 66.4 | [Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding](https://aclanthology.org/2021.acl-long.462.pdf) | [Official](https://github.com/AutoTemp/Shallow-Aggressive-Decoding) |
+| DeBERTa(L) + RoBERTa(L) + XLNet (Tarnavskyi et al., ACL 2022) | 65.3 | [Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correction](https://aclanthology.org/2022.acl-long.266) | [Official](https://github.com/MaksTarnavskyi/gector-large) |
 | Sequence tagging + token-level transformations + two-stage fine-tuning + XLNet, single model (Omelianchuk et al., BEA 2020) | 65.3 | [GECToR – Grammatical Error Correction: Tag, Not Rewrite](https://arxiv.org/pdf/2005.12592.pdf) | [Official](https://github.com/grammarly/gector) |
 | Transformer + Pre-train with Pseudo Data + BERT (Kaneko et al., ACL 2020) | 65.2 | [Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction](https://arxiv.org/pdf/2005.00987.pdf) | [Official](https://github.com/kanekomasahiro/bert-gec) |
 | Transformer + Pre-train with Pseudo Data (Kiyono et al., EMNLP 2019) | 65.0 | [An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction](https://arxiv.org/abs/1909.00502) | [Official](https://github.com/butsugiri/gec-pseudodata) |
@@ -128,6 +131,9 @@ Since current state-of-the-art systems rely on as much annotated learner data as
 
 | Model           | F0.5  |  Paper / Source | Code |
 | ------------- | :-----:| --- | :-----: |
+| ESC (Qorib et al., NAACL 2022) | 79.90| [Frustratingly Easy System Combination for Grammatical Error Correction](https://aclanthology.org/2022.naacl-main.143/) | [official](https://github.com/nusnlp/esc) |
+| TMTC (Lai et al., ACL Findings 2022) | 77.93 | [Type-Driven Multi-Turn Corrections for Grammatical Error Correction](https://aclanthology.org/2022.findings-acl.254) | [official](https://github.com/DeepLearnXMU/TMTC) |
+| DeBERTa(L) + RoBERTa(L) + XLNet (Tarnavskyi et al., ACL 2022) | 76.05 | [Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correction](https://aclanthology.org/2022.acl-long.266) | [Official](https://github.com/MaksTarnavskyi/gector-large) |
 | GECToR large without synthetic pre-training - ensemble (Tarnavskyi and Omelianchuk, 2021) | 76.05 | [Improving Sequence Tagging for Grammatical Error Correction](https://er.ucu.edu.ua/handle/1/2707) | [Official](https://github.com/MaksTarnavskyi/gector-large) |
 | T5 ([t5.1.1.xxl](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/released_checkpoints.md)) trained on [cLang-8](https://github.com/google-research-datasets/clang8) (Rothe et al., ACL-IJCNLP 2021) | 75.88 | [A Simple Recipe for Multilingual Grammatical Error Correction](https://arxiv.org/pdf/2106.03830.pdf) | [T5](https://github.com/google-research/text-to-text-transfer-transformer), [cLang-8](https://github.com/google-research-datasets/clang8) |
 | Tagged corruptions - ensemble (Stahlberg and Kumar, 2021)| 74.9 | [Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Models](https://www.aclweb.org/anthology/2021.bea-1.4.pdf)| [Official](https://github.com/google-research-datasets/C4_200M-synthetic-dataset-for-grammatical-error-correction) |