In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Q: The main contribution of our work is thus a new parallel data-to-text NLG corpus that (1) is more conversational, rather than information seeking or question answering, and thus more suitable for an open-domain dialogue system, (2) represents a new, unexplored domain which, however, has excellent potential for application in conversational agents, and (3) has high-quality, manually cleaned human-produced utterances.

A: How the authors made sure that corpus is clean despite being crowdsourced?
****
Q: 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.

A: How are the different components of the model trained? Is it trained end-to-end?
****
Q: Our system was ranked second in the competition only 0.3 BLEU points behind the winning team UPC-TALP. The relative low BLEU and high TER scores obtained by all teams are due to out-of-domain data provided in the competition which made the task equally challenging to all participants. The fact that out-of-domain data was provided by the organizers resulted in a challenging but interesting scenario for all participants.

A:
Does the use of out-of-domain data improve the performance of the method?
****