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.

Example Input: Unlike the SQuAD dataset, which only has one passage given a question, there are several related passages for each question in the MS-MARCO dataset. In addition to annotating the answer, MS-MARCO also annotates which passage is correct.
Question: Why MS-MARCO is different from SQuAD?
Example Output: there are several related passages for each question in the MS-MARCO dataset. MS-MARCO also annotates which passage is correct

Example Input: To support this claim, we measure our system's performance for datasets across various domains. The evaluations are done by managed judges who understands the knowledge base and then judge user queries relevance to the QA pairs (binary labels). Each query-QA pair is judged by two judges. We filter out data for which judges do not agree on the label. Chit-chat in itself can be considered as a domain. Thus, we evaluate performance on given KB both with and without chit-chat data (last two rows in Table TABREF19), as well as performance on just chit-chat data (2nd row in Table TABREF19).
Question: What experiments do the authors present to validate their system?
Example Output: we measure our system's performance for datasets across various domains evaluations are done by managed judges who understands the knowledge base and then judge user queries relevance to the QA pairs

Example Input: image feature pre-selection part which models the tendency where people focus to ask questions We propose to perform saliency-like pre-selection operation to alleviate the problems and model the RoI patterns. The image is first divided into $g imes g$ grids as illustrated in Figure. 2 . Taking $m imes m$ grids as a region, with $s$ grids as the stride, we obtain $n imes n$ regions, where $n=\left\lfloor
rac{g-m}{s}
ight
floor +1$ . We then feed the regions to a pre-trained ResNet BIBREF24 deep convolutional neural network to produce $n imes n imes d_I$ -dimensional region features, where $d_I$ is the dimension of feature from the layer before the last fully-connected layer.
Question: Does the new system utilize pre-extracted bounding boxes and/or features?
Example Output:
Yes