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)


[Q]: Our experiments are run using the English$\rightarrow $Italian/German portions of the MuST-C corpus BIBREF25, which is extracted from TED talks, using the same train/validation/test split as provided with the corpus (see Table TABREF18). As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De). 
 Question: What dataset do they use?
[A]: English$\rightarrow $Italian/German portions of the MuST-C corpus As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De)


[Q]: We use word2vec BIBREF0 to train the word embeddings. 
 Question: What kind of model do they build to expand abbreviations?
[A]:
word2vec BIBREF0