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.

Input: Consider Input: But we also showed that in some cases the saliency maps seem to not capture the important input features. The second observation we can make is that the saliency map doesn't seem to highlight the right things in the input for the summary it generates

Output: Is the explanation from saliency map correct?

Input: Consider Input: Table TABREF16 shows the recognition performance of naive multilingual approach using BLSTMP and VGG model against a monolingual model trained with BLSTMP. The results clearly indicate that having a better architecture such as VGG-BLSTM helps in improving multilingual performance. We used a character-level RNNLM, which was trained with 2-layer LSTM on character sequences.

Output: What architectures are explored to improve the seq2seq model?

Input: Consider Input: We attribute the significant performance gain of our model over the baseline to a combination of several factors as described below:
Output: Do they train a different training method except from scheduled sampling?