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.

[Q]: Probing into the assorted empirical results may help us discover some interesting phenomenons:

The advantage of pre-training gradually diminishes with the increase of labeled data BIBREF14, BIBREF17, BIBREF18.

Fixed representations yield better results than fine-tuning in some cases BIBREF24.

Overall, pre-training the Seq2Seq encoder outperforms pre-training the decoder BIBREF24, BIBREF17, BIBREF15, BIBREF16. 
 Question: What experimental phenomena are presented?
[A]: The advantage of pre-training gradually diminishes with the increase of labeled data Fixed representations yield better results than fine-tuning in some cases pre-training the Seq2Seq encoder outperforms pre-training the decoder


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


[Q]: We use the full-length ROUGE F1 and METEOR as our main evaluation metrics. 
 Question: What evaluation metrics do they use?
[A]:
ROUGE F1 METEOR