Let's say in an NLP problem, I have a question and some correct answers to that question (say, 10 correct answers).
Is there a way to get a new answer as input, and "calculate" whether it is correct?
Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. It only takes a minute to sign up.Sign up to join this community
This problem can be solved better if you also include the background text from where the questions have been picked up. Then, firstly train word embeddings on the background text. Further, generate sentence compositionality of the questions as well as their answers using a Recursive Neural Network or some variant. After all these steps you can compute the similarity between compositions of the new answer and given answers to know if it is correct or not.
One approach to consider:
You will need a similarity measure (say cosine similarity), S(A,B) between two pieces of text, and a way to threshold this measure.
The idea is that the new answer should be accepted if it is "similar" to other known answers. How similar should it be? This is estimated using the similarity between known answers. This is the broad approach. The similarity measure and threshold computation can be experimented with.