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: The second Turkish dataset is the Twitter corpus which is formed of tweets about Turkish mobile network operators. Those tweets are mostly much noisier and shorter compared to the reviews in the movie corpus. In total, there are 1,716 tweets. 973 of them are negative and 743 of them are positive. These tweets are manually annotated by two humans, where the labels are either positive or negative.
Question: What details are given about the Twitter dataset?

A: Those tweets are mostly much noisier and shorter compared to the reviews in the movie corpus. In total, there are 1,716 tweets. 973 of them are negative and 743 of them are positive.
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Q: 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?

A: gating function Dynamic Memory
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Q: We therefore use reduction in softmax probability of the correct relation as our signaling strength metric for the model. We call this metric ${\Delta }_s$ (for delta-softmax), which can be written as:

where $rel$ is the true relation of the EDU pair, $t_i$ represents the token at index $i$ of $N$ tokens, and $X_{mask=i}$ represents the input sequence with the masked position $i$ (for $i \in 1 \ldots N$ ignoring separators, or $\phi $, the empty set).
Question: How is the delta-softmax calculated?

A:
Answer with content missing: (Formula) Formula is the answer.
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