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.

Input: Consider Input: Unlike the SQuAD dataset, which only has one passage given a question, there are several related passages for each question in the MS-MARCO dataset. In addition to annotating the answer, MS-MARCO also annotates which passage is correct.  
 Question: Why MS-MARCO is different from SQuAD?

Output: there are several related passages for each question in the MS-MARCO dataset. MS-MARCO also annotates which passage is correct


Input: Consider Input: word embedding, input encoder, alignment, aggregation, and prediction 
 Question: what is the architecture of the baseline model?

Output: word embedding, input encoder, alignment, aggregation, and prediction.


Input: Consider Input: We further use as features the embeddings of the claim, of the best-scoring snippet, and of the best-scoring sentence triplet from a Web page. We calculate these embeddings (i) as the average of the embeddings of the words in the text, and also (ii) using LSTM encodings, which we train for the task as part of a deep neural network (NN). We also use a task-specific embedding of the claim together with all the above evidence about it, which comes from the last hidden layer of the NN. 
 Question: What algorithm and embedding dimensions are used to build the task-specific embeddings?


Output:  task-specific embedding of the claim together with all the above evidence about it, which comes from the last hidden layer of the NN