Does this actually make sense at all? Usually I figure out the meaning of a sentence to form an answer, which requires thinking about more that just the words in the given sentence.
So, I experimented with Skip-thought vectors (as question), to embody the the semantic knowledge about the question but that performed poorly, I mean very poorly. Tbh, I was surprised with that.
Currently the way system works, is highly biased towards the individual words in the question, whenever it sees the word "color" in the question it is going to pick one of the 'colors' as the answer, even if the question is not asking about colors. This is certainly due all the priors from training data.
Currently the way system works, is highly biased towards the individual words in the question, whenever it sees the word "color" in the question it is going to pick one of the 'colors' as the answer, even if the question is not asking about colors. This is certainly due all the priors from training data.