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.

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?
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

Our data set contains $3,209$ reviews about 553 different cars from 49 different car manufacturers. 
 Question: How big is dataset of car-speak language?
$3,209$ reviews 

The choice of two methods for our empirical study is motivated by the best performance achieved by Logistic Regression in question-question similarity at SemEval 2017 (best system BIBREF37 and second best system BIBREF38 ), and the high performance achieved by neural networks on larger datasets such as SNLI BIBREF13 , BIBREF39 , BIBREF40 , BIBREF41  
 Question: What machine learning and deep learning methods are used for RQE?

Logistic Regression neural networks