Update links in grammatical_error_correction.md

utkarshpant · Nov 9, 2018 · 13f55d6 · 13f55d6
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diff --git a/english/grammatical_error_correction.md b/english/grammatical_error_correction.md
@@ -12,18 +12,21 @@ GEC is usually formulated as a sentence-to-sentence correction task. A GEC syste
 
 The CoNLL-2014 shared task test set (https://www.comp.nus.edu.sg/~nlp/conll14st/conll14st-test-data.tar.gz) is the most widely used dataset to benchmark GEC systems.  The test set contains 1,312 english sentences with error annotations by 2 expert annotators. Models are evaluated with MaxMatch scorer ([Dahlmeier and Ng, 2012](http://www.aclweb.org/anthology/N12-1067)) which is a phrase-level F<sub>β</sub>-score with β=0.5 that weights precision twice as recall.
 
-The shared task setting restricts that systems use only publicly available datasets for training to fairer comparisons. The best published results on the CoNLL-2014 test set are given below. A distinction is made between papers that report results in the restricted CoNLL-2014 shared task setting using publicly-available training datasets (_Restricted_) and those that made use of large non-public datasets (_Unrestricted_).
+The shared task setting restricts that systems use only publicly available datasets for training to fairer comparisons. The best published results on the CoNLL-2014 test set are given below. A distinction is made between papers that report results in the restricted CoNLL-2014 shared task setting using publicly-available training datasets (_**Restricted**_) and those that made use of large non-public datasets (_**Unrestricted**_).
 
 
 | Model           | F0.5  |  Paper / Source | Code |
 | ------------- | :-----:| --- | :-----: |
 |_**Restricted**_     |      
-| CNN Seq2Seq + Quality Estimation (Chollampatt & Ng, EMNLP 2018) | 56.52 | [Neural Quality Estimation of Grammatical Error Correction](http://aclweb.org/anthology/D18-1274) | |
-| SMT + BiGRU (Grundkiewicz et al., 2018) |  56.25 | [Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation](https://arxiv.org/abs/1804.05945)| NA |
+| CNN Seq2Seq + Quality Estimation (Chollampatt and Ng, EMNLP 2018) | 56.52 | [Neural Quality Estimation of Grammatical Error Correction](http://aclweb.org/anthology/D18-1274) | [Official](https://github.com/nusnlp/neuqe/) |
+| SMT + BiGRU (Grundkiewicz and Junczys-Dowmunt, 2018) |  56.25 | [Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation](http://aclweb.org/anthology/N18-2046)| NA |
 | Transformer (Junczys-Dowmunt et al., 2018) | 55.8 | [Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task](http://aclweb.org/anthology/N18-1055)| NA |
-| CNN Seq2Seq (Chollampatt & Ng, 2018)| 54.79 | [ A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction](https://arxiv.org/abs/1801.08831)| [Official](https://github.com/nusnlp/mlconvgec2018) |
+| CNN Seq2Seq (Chollampatt and Ng, 2018)| 54.79 | [A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/17308/16137)| [Official](https://github.com/nusnlp/mlconvgec2018) |
 |_**Unrestricted**_            |
-| CNN Seq2Seq + Fluency Boost (Ge et al., 2018) |  61.34 | [Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study](https://arxiv.org/abs/1807.01270)| NA |
+| CNN Seq2Seq + Fluency Boost (Ge et al., 2018) |  61.34 | [Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study](https://arxiv.org/pdf/1807.01270.pdf)| NA |
+
+_**Restricted**_: uses only publicly available datasets. _**Unrestricted**_: uses non-public datasets.
+
 
 ### CoNLL-2014 10 Annotations
 
@@ -32,22 +35,27 @@ The shared task setting restricts that systems use only publicly available datas
 | Model           | F0.5  |  Paper / Source | Code |
 | ------------- | :-----:| --- | :-----: |
 |_**Restricted**_     |           
-| SMT + BiGRU (Grundkiewicz et al., 2018) |  72.04 | [Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation](https://arxiv.org/abs/1804.05945)| NA |
-| CNN Seq2Seq (Chollampatt & Ng, 2018)| 70.14 (measured by Ge et al., 2018) | [ A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction](https://arxiv.org/abs/1801.08831)| [Official](https://github.com/nusnlp/mlconvgec2018) |
+| SMT + BiGRU (Grundkiewicz and Junczys-Dowmunt, 2018) |  72.04 | [Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation](http://aclweb.org/anthology/N18-2046)| NA |
+| CNN Seq2Seq (Chollampatt and Ng, 2018)| 70.14 (measured by Ge et al., 2018) | [ A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/17308/16137)| [Official](https://github.com/nusnlp/mlconvgec2018) |
 |_**Unrestricted**_            |
-| CNN Seq2Seq + Fluency Boost (Ge et al., 2018) |  76.88 | [Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study](https://arxiv.org/abs/1807.01270)| NA |
+| CNN Seq2Seq + Fluency Boost (Ge et al., 2018) |  76.88 | [Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study](https://arxiv.org/pdf/1807.01270.pdf)| NA |
+
+_**Restricted**_: uses only publicly available datasets. _**Unrestricted**_: uses non-public datasets.
 
 
 ### JFLEG
 
-[JFLEG corpus](https://github.com/keisks/jfleg) by [Napoles et al., 2017](https://arxiv.org/abs/1702.04066) consists of 1,511 english sentences with annotations. Models are evaluated with [GLEU metric](https://arxiv.org/abs/1609.08144).
+[JFLEG test set](https://github.com/keisks/jfleg) released by [Napoles et al., 2017](http://aclweb.org/anthology/E17-2037) consists of 747 english sentences with 4 references for each sentence. Models are evaluated with [GLEU](https://github.com/cnap/gec-ranking/) metric ([Napoles et al., 2016](https://arxiv.org/pdf/1605.02592.pdf)).
 
 | Model           | GLEU  |  Paper / Source | Code |
 | ------------- | :-----:| --- | :-----: |
 |_**Restricted**_     |           
-| SMT + BiGRU (Grundkiewicz et al., 2018) |  61.50 | [Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation](https://arxiv.org/abs/1804.05945)| NA |
+| SMT + BiGRU (Grundkiewicz and Junczys-Dowmunt, 2018) |  61.50 | [Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation](http://aclweb.org/anthology/N18-2046)| NA |
 | Transformer (Junczys-Dowmunt et al., 2018) | 59.9 | [Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task](http://aclweb.org/anthology/N18-1055)| NA |
-| CNN Seq2Seq (Chollampatt & Ng, 2018)| 57.47 | [ A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction](https://arxiv.org/abs/1801.08831)| [Official](https://github.com/nusnlp/mlconvgec2018) |
+| CNN Seq2Seq (Chollampatt and Ng, 2018)| 57.47 | [ A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/17308/16137)| [Official](https://github.com/nusnlp/mlconvgec2018) |
 |_**Unrestricted**_           |
-| CNN Seq2Seq + Fluency Boost and inference (Ge et al., 2018) |  62.37 | [Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study](https://arxiv.org/abs/1807.01270)| NA |
+| CNN Seq2Seq + Fluency Boost and inference (Ge et al., 2018) |  62.37 | [Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study](https://arxiv.org/pdf/1807.01270.pdf)| NA |
+
+_**Restricted**_: uses only publicly available datasets. _**Unrestricted**_: uses non-public datasets.
+