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.

Q: 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?

A: name-based Nearest-Neighbor model (NN) Encoder-Decoder baseline with ingredient attention (Enc-Dec)
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Q: The grammar induction method works on the premise of curriculum learning BIBREF7 , where the parser first learns to parse simple sentences, then proceeds to learn more complex ones. The induction method is iterative, semi-automatic and based on frequent patterns. A context-free grammar (CFG) is induced from the text, which is represented by several layers of semantic annotations. 
 Question: How did they induce the CFG?

A: the parser first learns to parse simple sentences, then proceeds to learn more complex ones. The induction method is iterative, semi-automatic and based on frequent patterns
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Q: Our first baseline is ROUGE-L BIBREF1 , since it is the most commonly used metric for compression tasks. We compare to the best n-gram-overlap metrics from toutanova2016dataset; We further compare to the negative LM cross-entropy, i.e., the log-probability which is only normalized by sentence length. Our next baseline is perplexity, which corresponds to the exponentiated cross-entropy: Due to its popularity, we also performed initial experiments with BLEU BIBREF17 .  
 Question: Is ROUGE their only baseline?

A:
No
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