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:
For training data, 3 million webpages from this corpus were processed with a CCG parser to produce logical forms BIBREF10 . We also used the test set created by Krishnamurthy and Mitchell, which contains 220 queries generated in the same fashion as the training data from a separate section of ClueWeb. However, as they did not release a development set with their data, we used this set as a development set. This final test set contains 307 queries.
Question: How big is their dataset?

Ex Output:
3 million webpages processed with a CCG parser for training, 220 queries for development, and 307 queries for testing

Ex Input:
In particular, we aggregate documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions. We name this dataset STORIES since most of the constituent documents take the form of a story with long chain of coherent events. Figure 5 -left and middle show that STORIES always yield the highest accuracy for both types of input processing.
Question: Which of their training domains improves performance the most?

Ex Output:
documents from the CommonCrawl dataset that has the most overlapping n-grams with the question

Ex Input:
Results show that the CJFA encoder obtains significantly better phone classification accuracy than the VAE baseline and also than the CJFS encoder. These results are replicated for speaker recognition tasks.
Question: Which approach out of two proposed in the paper performed better in experiments?

Ex Output:
CJFA encoder