Exploring Answer Information Methods for Question Generation with
  Transformers
Talha Chafekar Aafiya Hussain Grishma Sharma Deepak Sharma
There has been a lot of work in question generation where different methods
to provide target answers as input, have been employed. This experimentation
has been mostly carried out for RNN based models. We use three different
methods and their combinations for incorporating answer information and explore
their effect on several automatic evaluation metrics. The methods that are used
are answer prompting, using a custom product method using answer embeddings and
encoder outputs, choosing sentences from the input paragraph that have answer
related information, and using a separate cross-attention attention block in
the decoder which attends to the answer. We observe that answer prompting
without any additional modes obtains the best scores across rouge, meteor
scores. Additionally, we use a custom metric to calculate how many of the
generated questions have the same answer, as the answer which is used to
generate them.