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 Q]: The experimental results are shown in Table 1, where it can be seen that autoencoders outperformed MLP and CNN outperformed autoencoders with the highest achieved accuracy of 82.6%. 
 Question: What was their performance on the dataset?
[EX A]: accuracy of 82.6%

[EX Q]: 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 A]: 3 million webpages processed with a CCG parser for training, 220 queries for development, and 307 queries for testing

[EX Q]: We report segmentation performance using precision, recall, and F-measure on boundaries (BP, BR, BF), and tokens (WP, WR, WF). We also report the exact-match (X) metric which computes the proportion of correctly segmented utterances. 
 Question: How is the word segmentation task evaluated?
[EX A]:
precision, recall, and F-measure on boundaries (BP, BR, BF), and tokens (WP, WR, WF)  exact-match (X) metric