sachin:

both the questions and answers being similar enough for me to think that I could use a seq2seq model to suggest answers.


Dear Sachin,
The models that we train are only as good as the data that we train them on.  So start by thinking about your data.
The assumption of your dataset is that a sequence-to-sequence model can predict the answer to a question.  In practice, sequence-to-sequence models were originally designed for translation. They were designed to predict an equivalent sequence.
So I would expect that a sequence-to-sequence model would only predict the correct answer if the question contains the information necessary to predict that correct answer.
Perhaps instead you could classify the answers?  If so, you could train a model to predict the correct classification of the question.
Or you might look at Facebook’s BART model 4.  It’s a sequence-to-sequence model, but at inference time, they use it to predict a classification.
And what’s really cool is that it’s a zero-shot classification.  The model did not see the classifications during training.  So in practice you might find many uses for the trained model.
Best wishes,
- Eryk