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.

Q: We collected the dataset with 1900 dialogs and 8533 turns. 

A: What is the average length of dialog?
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Q: What is the impact of pre-trained representations with less transcribed data? In order to get a better understanding of this, we train acoustic models with different amounts of labeled training data and measure accuracy with and without pre-trained representations (log-mel filterbanks). The pre-trained representations are trained on the full Librispeech corpus and we measure accuracy in terms of WER when decoding with a 4-gram language model. Figure shows that pre-training reduces WER by 32% on nov93dev when only about eight hours of transcribed data is available. Pre-training only on the audio data of WSJ ( WSJ) performs worse compared to the much larger Librispeech ( Libri). This further confirms that pre-training on more data is crucial to good performance.

A: Do they explore how much traning data is needed for which magnitude of improvement for WER? 
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Q: Another approach for generating query and image representations is treating images as a black box. Given the statistics of our dataset (3B query, image pairs with 220M unique queries and 900M unique images), we know that different queries co-occur with the same images. Intuitively, if a query $q_1$ co-occurs with many of the same images as query $q_2$ , then $q_1$ and $q_2$ are likely to be semantically similar, regardless of the visual content of the shared images. Thus, we can use a method that uses only co-occurrence statistics to better understand how well we can capture relationships between queries.

A:
Could you learn such embedding simply from the image annotations and without using visual information?
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