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.
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Question: 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?

Answer: 133,287 images

Question: Fenerbahce We have decided to consider tweets about popular sports clubs as our domain for stance detection. Hence we have determined our targets as Galatasaray (namely Target-1) and Fenerbahce (namely, Target-2) which are two of the most popular football clubs in Turkey. Then, we have annotated the stance information in the tweets for these targets as Favor or Against. For the purposes of the current study, we have not annotated any tweets with the Neither class.
Question: How were the tweets annotated?

Answer: tweets are annotated with only Favor or Against for two targets - Galatasaray and Fenerbahce

Question: For estimating the sense of the word INLINEFORM0 in a sentence, we search for such a synset INLINEFORM1 that maximizes the cosine similarity to the sentence vector: DISPLAYFORM0
Question: What measure of semantic similarity is used?

Answer:
cosine similarity