We are using Google BERT for question and answering. We are using vanialla bert-base-uncased as well as squad trained checkpoints.
The answers from BERT are very short and crisp. For example, if we ask describe a chatbot, then it will simply return, takes input from user and replies ... Though the answer actually is complete paragraph.
- Will training BERT on long paragraphs can solve this problem? If yes, any idea from where we can get such a QnA dataset, as it is not possible to create a huge QnA dataset manually.
- Is there any other tweak which can be done at some BERT layer, so that it starts understanding a long answer?
- Or is there any other framework or system already have solved this kind of problem, by maybe integrating with some other neural network technique, or by using a pipeline of multiple components?