Fixed headlines, model names

semantic_parsing.md

@@ -9,34 +9,35 @@ such as SQL or more abstract representations such as [Abstract Meaning Represent
 ### LDC2014T12
 
 Models are evaluated on the newswire section and the full dataset based on F1.
-Systems marked with \* are pipeline systems that require a dependency parse as input
+Brackets indicates the preprocessed features a model uses.
+Systems marked with \* are pipeline systems that require a dependency parse as input.
 
 | Model           | F1 Newswire  | F1 Full |  Paper / Source |
 | ------------- | :-----:| :-----:| --- |
-| Zhou et al. (2016) (POS, DEP, NER, SRL)* | 0.71 | 0.66 | [AMR Parsing with an Incremental Joint Model](https://aclweb.org/anthology/D16-1065) |
-| Wang et al. (2015) (POS, DEP, NER, SRL)* | 0.70 | 0.66 | [Boosting Transition-based AMR Parsing with Refined Actions and Auxiliary Analyzers](http://www.aclweb.org/anthology/P15-2141) |
-| Stack-LSTM (Ballesteros and Al-Onaizan, 2017) (POS, DEP) | 0.69 | 0.64  | [AMR Parsing using Stack-LSTMs](http://www.aclweb.org/anthology/D17-1130) |
+| Incremental joint model (POS, DEP, NER, SRL) (Zhou et al., 2016)* | 0.71 | 0.66 | [AMR Parsing with an Incremental Joint Model](https://aclweb.org/anthology/D16-1065) |
+| Augmented parser (POS, DEP, NER, SRL) (Wang et al., 2015)* | 0.70 | 0.66 | [Boosting Transition-based AMR Parsing with Refined Actions and Auxiliary Analyzers](http://www.aclweb.org/anthology/P15-2141) |
+| Stack-LSTM (POS, DEP) (Ballesteros and Al-Onaizan, 2017) | 0.69 | 0.64  | [AMR Parsing using Stack-LSTMs](http://www.aclweb.org/anthology/D17-1130) |
 
-# LDC2015E86
+### LDC2015E86
 
 Models are evaluated based on smatch.
 
 | Model           | Smatch  |  Paper / Source |
 | ------------- | :-----:| --- |
-Ours | 73.7 | [AMR Parsing as Graph Prediction with Latent Alignment](https://arxiv.org/abs/1805.05286) |
+Joint model (Lyu and Titov, 2018) | 73.7 | [AMR Parsing as Graph Prediction with Latent Alignment](https://arxiv.org/abs/1805.05286) |
 Mul-BiLSTM (Foland and Martin, 2017) | 70.7 | [Abstract Meaning Representation Parsing using LSTM Recurrent Neural Networks](http://aclweb.org/anthology/P17-1043) |
 JAMR (Flanigan et al., 2016) | 67.0 | [CMU at SemEval-2016 Task 8: Graph-based AMR Parsing with Infinite Ramp Loss](http://www.aclweb.org/anthology/S16-1186) |
 CAMR (Wang et al., 2016) | 66.5 | [CAMR at SemEval-2016 Task 8: An Extended Transition-based AMR Parser](http://www.aclweb.org/anthology/S16-1181) |
 AMREager (Damonte et al., 2017) | 64.0 | [An Incremental Parser for Abstract Meaning Representation](http://www.aclweb.org/anthology/E17-1051) |
 SEQ2SEQ + 20M (Konstas et al., 2017) | 62.1 | [Neural AMR: Sequence-to-Sequence Models for Parsing and Generation](https://arxiv.org/abs/1704.08381) |
 
-# LDC2016E25
+### LDC2016E25
 
 Results are computed over 8 runs. Models are evaluated based on smatch.
 
 | Model           | Smatch  |  Paper / Source |
 | ------------- | :-----:| --- |
-Ours | 74.4 | [AMR Parsing as Graph Prediction with Latent Alignment](https://arxiv.org/abs/1805.05286) |
+Joint model (Lyu and Titov, 2018) | 74.4 | [AMR Parsing as Graph Prediction with Latent Alignment](https://arxiv.org/abs/1805.05286) |
 ChSeq + 100K (van Noord and Bos, 2017) | 71.0 | [Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations](https://arxiv.org/abs/1705.09980) |
 Neural-Pointer (Buys and Blunsom, 2017) | 61.9 | [Oxford at SemEval-2017 Task 9: Neural AMR Parsing with Pointer-Augmented Attention](http://aclweb.org/anthology/S17-2157) |