Add results from "Semi-Supervised Sequence Modeling with Cross-View T…

…raining" (sebastianruder#182)

english/ccg_supertagging.md

@@ -19,6 +19,7 @@ Performance is only calculated on the 425 most frequent labels. Models are evalu
 
 | Model           | Accuracy |  Paper / Source |
 | ------------- | :-----:| --- |
+| Clark et al. (2018) | 96.1 | [Semi-Supervised Sequence Modeling with Cross-View Training](https://arxiv.org/abs/1809.08370) |
 | Lewis et al. (2016) | 94.7 | [LSTM CCG Parsing](https://aclweb.org/anthology/N/N16/N16-1026.pdf) |
 | Vaswani et al. (2016) | 94.24 | [Supertagging with LSTMs](https://aclweb.org/anthology/N/N16/N16-1027.pdf) |
 | Low supervision (Søgaard and Goldberg, 2016) | 93.26 | [Deep multi-task learning with low level tasks supervised at lower layers](http://anthology.aclweb.org/P16-2038) |

english/dependency_parsing.md

@@ -29,6 +29,7 @@ labeled attachment score (LAS). Here, we also mention the predicted POS tagging
 
 | Model           | POS | UAS | LAS | Paper / Source | Code |
 | ------------- | :-----: | :-----:| :-----:| --- | --- |
+| CVT + Multi-Task (Clark et al., 2018) | ---  | 96.61 | 95.02 | [Semi-Supervised Sequence Modeling with Cross-View Training](https://arxiv.org/abs/1809.08370) | [Official](https://github.com/tensorflow/models/tree/master/research/cvt_text) |
 | Deep Biaffine (Dozat and Manning, 2017) | 97.3 | 95.44 | 93.76 | [Deep Biaffine Attention for Neural Dependency Parsing](https://arxiv.org/abs/1611.01734) | [Official](https://github.com/tdozat/Parser-v1) |
 | jPTDP (Nguyen and Verspoor, 2018) | 97.97 | 94.51 | 92.87  | [An improved neural network model for joint POS tagging and dependency parsing](https://arxiv.org/abs/1807.03955) | [Official](https://github.com/datquocnguyen/jPTDP) |
 | Andor et al. (2016) | 97.44 | 94.61 | 92.79 | [Globally Normalized Transition-Based Neural Networks](https://www.aclweb.org/anthology/P16-1231) | |

english/named_entity_recognition.md

@@ -19,7 +19,7 @@ corpus tagged with four different entity types (PER, LOC, ORG, MISC). Models are
 | ------------- | :-----:| --- | --- |
 | Flair embeddings (Akbik et al., 2018) | 93.09 | [Contextual String Embeddings for Sequence Labeling](https://drive.google.com/file/d/17yVpFA7MmXaQFTe-HDpZuqw9fJlmzg56/view) | [Flair framework](https://github.com/zalandoresearch/flair)
 | BERT Large (Devlin et al., 2018) | 92.8 | [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) | |
-| CVT + Multi (Clark et al., 2018) | 92.6 | [Semi-Supervised Sequence Modeling with Cross-View Training](https://arxiv.org/abs/1809.08370) |  |
+| CVT + Multi-Task (Clark et al., 2018) | 92.61 | [Semi-Supervised Sequence Modeling with Cross-View Training](https://arxiv.org/abs/1809.08370) | [Official](https://github.com/tensorflow/models/tree/master/research/cvt_text) |
 | BERT Base (Devlin et al., 2018) | 92.4 | [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) | |
 | BiLSTM-CRF+ELMo (Peters et al., 2018) | 92.22 | [Deep contextualized word representations](https://arxiv.org/abs/1802.05365) | [AllenNLP Project](https://allennlp.org/elmo) [AllenNLP GitHub](https://github.com/allenai/allennlp) |
 | Peters et al. (2017) | 91.93 | [Semi-supervised sequence tagging with bidirectional language models](https://arxiv.org/abs/1705.00108) | |
@@ -57,6 +57,7 @@ The [Ontonotes corpus v5](https://catalog.ldc.upenn.edu/docs/LDC2013T19/OntoNote
 
 | Model           | F1  |  Paper / Source | Code |
 | ------------- | :-----:| --- | --- |
+| CVT + Multi-Task (Clark et al., 2018) | 88.81 | [Semi-Supervised Sequence Modeling with Cross-View Training](https://arxiv.org/abs/1809.08370)  | [Official](https://github.com/tensorflow/models/tree/master/research/cvt_text) |
 | Bi-LSTM-CRF + Lexical Features (Ghaddar and Langlais 2018) | 87.95 | [Robust Lexical Features for Improved Neural Network Named-Entity Recognition](https://arxiv.org/pdf/1806.03489.pdf) | |
 | BiLSTM-CRF (Strubell et al, 2017) | 86.99 | [Fast and Accurate Entity Recognition with Iterated Dilated Convolutions](https://arxiv.org/pdf/1702.02098.pdf)  | [Official](https://github.com/iesl/dilated-cnn-ner) |
 | Iterated Dilated CNN (Strubell et al, 2017) | 86.84 | [Fast and Accurate Entity Recognition with Iterated Dilated Convolutions](https://arxiv.org/pdf/1702.02098.pdf)  | [Official](https://github.com/iesl/dilated-cnn-ner) |