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.
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Question: To overcome the above-mentioned challenges, we propose a locally-linear meta-embedding learning method that (a) requires only the words in the vocabulary of each source embedding, without having to predict embeddings for missing words, (b) can meta-embed source embeddings with different dimensionalities, (c) is sensitive to the diversity of the neighbourhoods of the source embeddings.

Our proposed method comprises of two steps: a neighbourhood reconstruction step (Section "Nearest Neighbour Reconstruction" ), and a projection step (Section "Projection to Meta-Embedding Space" ). In the reconstruction step, we represent the embedding of a word by the linearly weighted combination of the embeddings of its nearest neighbours in each source embedding space. 
 Question: What is the introduced meta-embedding method introduced in this paper?

Answer: proposed method comprises of two steps: a neighbourhood reconstruction step (Section "Nearest Neighbour Reconstruction" ), and a projection step (Section "Projection to Meta-Embedding Space" ). In the reconstruction step, we represent the embedding of a word by the linearly weighted combination of the embeddings of its nearest neighbours in each source embedding space. 


Question: Note that ESIM models have two LSTM layers, the first (input) LSTM performs the input encoding and the second (inference) LSTM generates the representation for inference. 
 Question: How many layers are there in their model?

Answer: two LSTM layers


Question: The overall Total Accuracy score reported in table TABREF19 using the entire feature set is 549.  
 Question: Do they experiment with the dataset?

Answer:
Yes