TASK DEFINITION: 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.
PROBLEM: EmotionLines BIBREF6 is a dialogue dataset composed of two subsets, Friends and EmotionPush, according to the source of the dialogues. 
 Question: what datasets were used?

SOLUTION: Friends EmotionPush

PROBLEM: Contexts are either ground-truth contexts from that dataset, or they are Wikipedia passages retrieved using TF-IDF BIBREF24 based on a HotpotQA question. A new non-overlapping set of contexts was again constructed from Wikipedia via HotpotQA using the same method as Round 1. In addition to contexts from Wikipedia for Round 3, we also included contexts from the following domains: News (extracted from Common Crawl), fiction (extracted from BIBREF27, and BIBREF28), formal spoken text (excerpted from court and presidential debate transcripts in the Manually Annotated Sub-Corpus (MASC) of the Open American National Corpus), and causal or procedural text, which describes sequences of events or actions, extracted from WikiHow. Finally, we also collected annotations using the longer contexts present in the GLUE RTE training data, which came from the RTE5 dataset BIBREF29. 
 Question: What data sources do they use for creating their dataset?

SOLUTION: Wikipedia (of 250-600 characters) from the manually curated HotpotQA training set Manually Annotated Sub-Corpus (MASC) of the Open American National Corpus) RTE5

PROBLEM: We propose a natural language generation model based on BERT, making good use of the pre-trained language model in the encoder and decoder process, and the model can be trained end-to-end without handcrafted features. During model training, the objective of our model is sum of the two processes, jointly trained using "teacher-forcing" algorithm. During training we feed the ground-truth summary to each decoder and minimize the objective.

$$L_{model} = \hat{L}_{dec} + \hat{L}_{refine}$$ (Eq. 23)

At test time, each time step we choose the predicted word by $\hat{y} = argmax_{y^{\prime }} P(y^{\prime }|x)$ , use beam search to generate the draft summaries, and use greedy search to generate the refined summaries. 
 Question: How are the different components of the model trained? Is it trained end-to-end?

SOLUTION:
the objective of our model is sum of the two processes, jointly trained using "teacher-forcing" algorithm we feed the ground-truth summary to each decoder and minimize the objective At test time, each time step we choose the predicted word by $\hat{y} = argmax_{y^{\prime }} P(y^{\prime }|x)$ , use beam search to generate the draft summaries, and use greedy search to generate the refined summaries. the model can be trained end-to-end