This paper proposes a neural conversational model that uses a sequence-to-sequence framework to generate answers to questions. It can be trained end-to-end without many hand-crafted rules and can generate simple conversations given large training data. The model adopts the sequence-to-sequence framework and is trained using backpropagation to maximize cross entropy. During inference it uses greedy decoding but beam search could also be used. The model was verified by having humans rate it against CleverBot on 200 questions and it can generate basic conversations and answer questions without rules.

1. A Neural
Conversational Model

2. What is this paper about?
 It proposed a neural conversational model which generates an answer to a question using
sequence to sequence framework.

3. What’s better than previous paper?
 This model can be trained end-to-end and thus requires much fewer hand-craft rules.
 This model can generate simple conversations given a large conversational training dataset.
 This model has its simplicity and generality and thus can be applied to machine translation,
question/answering, and conversations without major changes in the architecture.

4. What’re important parts of
technic and method?
 This model adopted sequence to sequence framework and be done by backpropagation in
learning to maximize the cross entropy of the correct sequence.
 During inference, this model used a greedy inference approach.
 A less greedy approach would be to use beam search.

5. How did they verify it?
 They Asked four different humans to rate this model versus CleverBot with picked 200 questions
from http://ai.stanford.edu/~quocle/QAresults.pdf.

6. Is there a discussion?
 It can generate simple and basic conversations, extract knowledge and answer to many types of
questions without any rules.

7. Is there a paper to read next?
 Neural machine translation
 RNNs
 LSTM
 Sequence to sequence framework

8. Paper Information
 https://arxiv.org/abs/1506.05869