it is based on End-to-End Memory Networks. And I extend this model using Skip-thought Vector for dealing with sentence answer.
We use the "PandaQA" dataset. Unfortunately, it is not available due to the Licence issues of the owner. The PandaQA dataset is similar with the bAbI dataset of QA tasks in terms of dialog-based question answering dataset. But answers in the PandaQA dataset is a sentence, not a word.
The objective function of this model is to learn the representation of the answer sentence according to the question. The skip-thoughts vector is used as a sentence encoder. And we can infer the answer representation with the End-to-End Memory Networks architecture. The final answer is choosed as the nearest answer candidate on training dataset.
You can check the details about the model on the article file, which is a Korean version article and the English version is on preparing.
