My goal is to predict the most appropriate answer from an utterance, in a group of 21 potential answers. (I'm not sure the "question" is called utterance though. )
Utterance: How are you today? Answers: Answer1, 2, ..., 21.
I have a training file with this format:
Utterance: Answers: Good answer, wrong answer1, wrong answer2,..., wrong answer20.
For the first time, we have to make a prediction from a group of possible answers, and, thus, this is a MCQ form.
Any ideas how I could start the problem?
What I've done
For the moment, the only thing I did was to choose the answers from the 21 possible answers which had the highest cosine similarity with the utterance. (So, unsupervised). It's not that bad (24% against 1/21 at random), but I'm sure there are ways to make something really better.
What I don't want to do at first
Use a generative model which predicts a full sentence. I want to choose the best candidate amongs the 21 answers, and use the training file which can allow us to do supervised learning.