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.

[EX 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.
[EX A]: How the authors made sure that corpus is clean despite being crowdsourced?

[EX Q]: Table TABREF16 shows the recognition performance of naive multilingual approach using BLSTMP and VGG model against a monolingual model trained with BLSTMP. The results clearly indicate that having a better architecture such as VGG-BLSTM helps in improving multilingual performance. We used a character-level RNNLM, which was trained with 2-layer LSTM on character sequences.
[EX A]: What architectures are explored to improve the seq2seq model?

[EX Q]: In this method, we simply concatenate the corpora of multiple domains with two small modifications: a. Appending the domain tag “<2domain>" to the source sentences of the respective corpora. 
[EX A]:
How did they use the domain tags?