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: Suppose there are INLINEFORM0 mentions in documents on average, among these global models, NCEL not surprisingly has the lowest time complexity INLINEFORM1 since it only considers adjacent mentions, where INLINEFORM2 is the number of sub-GCN layers indicating the iterations until convergence.
Question: Do they only use adjacent entity mentions or use more than that in some cases (next to adjacent)?

Answer: NCEL considers only adjacent mentions.

Question: This model uses a particular RNN cell in order to store just relevant information about the given question. Dynamic Memory stores information of entities present in $T$ . The addition of the $s_t^T q$ term in the gating function is our main contribution.
Question: How does the model recognize entities and their relation to answers at inference time when answers are not accessible?

Answer: gating function Dynamic Memory

Question: We adopt the following two datasets:

Quora BIBREF1: The Quora Question Pairs dataset contains question pairs annotated with labels indicating whether the two questions are paraphrases. We use the same dataset partition as BIBREF5, with 384,348/10,000/10,000 pairs in the training/development/test set respectively.

MRPC BIBREF34: The Microsoft Research Paraphrase Corpus consists of sentence pairs collected from online news. Each pair is annotated with a label indicating whether the two sentences are semantically equivalent. There are 4,076/1,725 pairs in the training/test set respectively.
Question: What are benhmark datasets for paraphrase identification?

Answer:
Quora MRPC