In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Ex Input:
We compare our personalized models against two baselines. The first is a name-based Nearest-Neighbor model (NN). We initially adapted the Neural Checklist Model of BIBREF0 as a baseline; however, we ultimately use a simple Encoder-Decoder baseline with ingredient attention (Enc-Dec), which provides comparable performance and lower complexity. 
 Question: What are the baseline models?

Ex Output:
name-based Nearest-Neighbor model (NN) Encoder-Decoder baseline with ingredient attention (Enc-Dec)


Ex Input:
The test-set accuracies obtained by different learning methods, including the current state-of-the-art results, are presented in Table TABREF11 . 
 Question: what models did they compare to?

Ex Output:
High-order CNN, Tree-LSTM, DRNN, DCNN, CNN-MC, NBoW and SVM 


Ex Input:
Using an encoder-decoder backbone, our model may be regarded as an extension of the constituent parsing model of BIBREF18 as shown in Figure FIGREF4. The difference is that in our model both constituent and dependency parsing share the same token representation and shared self-attention layers and each has its own individual Self-Attention Layers and subsequent processing layers. Our model includes four modules: token representation, self-attention encoder, constituent and dependency parsing decoder. Constituent Parsing Decoder Dependency Parsing Decoder 
 Question: What are the models used to perform constituency and dependency parsing?

Ex Output:

token representation self-attention encoder, Constituent Parsing Decoder  Dependency Parsing Decoder