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.

Ex Input:
We use Waseem and Hovy BIBREF5, Davidson et al. BIBREF9, and Waseem et al. BIBREF10 as baselines and compare the results with our different fine-tuning strategies using pre-trained BERTbase model.
Question: What baseline is used?

Ex Output:
Waseem and Hovy BIBREF5 Davidson et al. BIBREF9 Waseem et al. BIBREF10

Ex Input:
We use the VisDial v1.0 BIBREF0 dataset to train our models, where one example has an image with its caption, 9 question-answer pairs, and follow-up questions and candidate answers for each round. At round $r$, the caption and the previous question-answer pairs become conversational context. The whole dataset is split into 123,287/2,000/8,000 images for train/validation/test, respectively. Unlike the images in the train and validation sets, the images in the test set have only one follow-up question and candidate answers and their corresponding conversational context.
Question: How big is dataset for this challenge?

Ex Output:
133,287 images

Ex 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?

Ex Output:
there are several related passages for each question in the MS-MARCO dataset. MS-MARCO also annotates which passage is correct