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]: For the emotion recognition from text, we manually transcribe all utterances of our AMMER study. To exploit existing and available data sets which are larger than the AMMER data set, we develop a transfer learning approach. We use a neural network with an embedding layer (frozen weights, pre-trained on Common Crawl and Wikipedia BIBREF36), a bidirectional LSTM BIBREF37, and two dense layers followed by a soft max output layer. This setup is inspired by BIBREF38. We use a dropout rate of 0.3 in all layers and optimize with Adam BIBREF39 with a learning rate of $10^{-5}$ (These parameters are the same for all further experiments). We build on top of the Keras library with the TensorFlow backend. We consider this setup our baseline model. 
 Question: What is the baseline method for the task?
[EX A]: For the emotion recognition from text they use described neural network as baseline.
For audio and face there is no baseline.

[EX Q]: Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs.  
 Question: How big is QA-CTS task dataset?
[EX A]: 17,833 sentences, 826,987 characters and 2,714 question-answer pairs

[EX Q]: Through experiments on two large complex cross-domain datasets, SParC BIBREF2 and CoSQL BIBREF6, we carefully compare and analyze the performance of different context modeling methods.  
 Question: What two large datasets are used for evaluation?
[EX A]:
SParC BIBREF2 and CoSQL BIBREF6

