instruction:
In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
question:
Later, BIBREF8 introduced an RNN with an external stack memory to learn simple context-free languages, such as $a^n b^m$ , $a^nb^ncb^ma^m$ , and $a^{n+m} b^n c^m$ . Similar studies BIBREF15 , BIBREF16 , BIBREF17 , BIBREF10 , BIBREF11 have explored the existence of stable counting mechanisms in simple RNNs, which would enable them to learn various context-free and context-sensitive languages BIBREF9 , on the other hand, proposed a variant of Long Short-Term Memory (LSTM) networks to learn two context-free languages, $a^n b^n$ , $a^n b^m B^m A^n$ , and one strictly context-sensitive language, $a^n b^n c^n$ .
answer:
How do they get the formal languages?

question:
In the sentiment analysis experiments, we found that a tweet may contain multiple sentiments. The groundtruth labels contain 210 positive sentiments, 521 neutral sentiments, and 305 negative sentiments to the candidates.
answer:
Is datasets for sentiment analysis balanced?

question:
The theorem is proven constructively by selecting the parameters of the multi-head self-attention layer so that the latter acts like a convolutional layer.
answer:
How they prove that multi-head self-attention is at least as powerful as convolution layer?