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: For the NMT experiment, we used the Torch implementation to train a 2-layer LSTM model with 500 hidden units in both encoder and decoder, with 12 epochs.

Output: Which NMT models did they experiment with?


Input: Consider Input: This study assumes that a robot does not have any vocabularies in advance but can recognize syllables or phonemes.

Output: Does their model start with any prior knowledge of words?


Input: Consider Input: We consider pre-training on the audio data (without labels) of WSJ, part of clean Librispeech (about 80h) and full Librispeech as well as a combination of all datasets (§ SECREF7 ).  Our experimental results on the WSJ benchmark demonstrate that pre-trained representations estimated on about 1,000 hours of unlabeled speech can substantially improve a character-based ASR system and outperform the best character-based result in the literature, Deep Speech 2. 


Output: Which unlabeled data do they pretrain with?