Notes for paper titled A Neural Conversational Model

A Neural Conversational Model.md

A Neural Conversational Model

Introduction

• The paper presents a domain agnostic approach for conversational modelling based on Sequence to Sequence Learning Framework.
• Link to the paper

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.