TASK DEFINITION: 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.
PROBLEM: 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?

SOLUTION: two LSTM layers

PROBLEM: Three different datasets have been used to train our models: the Toronto book corpus, Wikipedia sentences and tweets. Our Sent2Vec models also on average outperform or are at par with the C-PHRASE model, despite significantly lagging behind on the STS 2014 WordNet and News subtasks. This observation can be attributed to the fact that a big chunk of the data that the C-PHRASE model is trained on comes from English Wikipedia, helping it to perform well on datasets involving definition and news items.
Question: Do they report results only on English data?

SOLUTION: Yes

PROBLEM: The training data released for this task contains 22 drug labels, referred to as Training-22, with gold standard annotations. As Training-22 is a relatively small dataset, we additionally utilize an external dataset with 180 annotated drug labels dubbed NLM-180 BIBREF5 (more later).
Question: What training data did they use?

SOLUTION:
Training-22 NLM-180