#### Introduction

* The paper presents a domain agnostic approach for conversational modelling based on [Sequence to Sequence Learning Framework](https://gist.github.com/shagunsodhani/e3608ccf262d6e5a6b537128c917c92c).
* [Link to the paper](http://arxiv.org/abs/1506.05869)

#### Model

* Neural Conversational Model (NCM)
* A Recurrent Neural Network (RNN) reads the input sentence, one token at a time, and predicts the output sequence, one    token at a time.
* Learns by backpropagation.
* The model maximises the cross entropy of correct sequence given its context.
* Greedy inference approach where predicted output token is used as input to predict the next output token.

#### Dataset

* IT HelpDesk dataset of conversations about computer related issues.
* OpenSubtitles dataset containing movie conversations.

#### Results

* The paper has reported some samples of conversations generated by the interaction between human actor and the NCM.
* NCM reports lower perplexity as compared to n-grams model.
* NCM outperforms CleverBot in a subjective test involving human evaluators to grade the two systems.

#### Strengths

* Domain-agnostic.
* End-To-End training without handcrafted rules.
* Underlying architecture (Sequence To Sequence Framework) can be leveraged for machine translation, question answering etc.

#### Weakness

* The responses are simple, short and at times inconsistent.
* The objective function of Sequence To Sequence Framework is not designed to capture the objective of conversational models.