## Deep Learning for Chatbots, Part 2 – Implementing a Retrieval-Based Model in Tensorflow

The Code and data for this tutorial is on Github.

#### Retrieval-Based bots

In this post we’ll implement a retrieval-based bot. Retrieval-based models have a repository of pre-defined responses they can use, which is unlike generative models that can generate responses they’ve never seen before. A bit more formally, the input to a retrieval-based model is a context $c$ (the conversation up to this point) and a potential response $r$. The model outputs is a score for the response. To find a good response you would calculate the score for multiple responses and choose the one with the highest score.

## Deep Learning for Chatbots, Part 1 – Introduction

Chatbots, also called Conversational Agents or Dialog Systems, are a hot topic. Microsoft is making big bets on chatbots, and so are companies like Facebook (M), Apple (Siri), Google, WeChat, and Slack. There is a new wave of startups trying to change how consumers interact with services by building consumer apps like Operator or x.ai, bot platforms like Chatfuel, and bot libraries like Howdy’s Botkit. Microsoft recently released their own bot developer framework.

Many companies are hoping to develop bots to have natural conversations indistinguishable from human ones, and many are claiming to be using NLP and Deep Learning techniques to make this possible. But with all the hype around AI it’s sometimes difficult to tell fact from fiction.

In this series I want to go over some of the Deep Learning techniques that are used to build conversational agents, starting off by explaining where we are right now, what’s possible, and what will stay nearly impossible for at least a little while. This post will serve as an introduction, and we’ll get into the implementation details in upcoming posts.

## Attention and Memory in Deep Learning and NLP

A recent trend in Deep Learning are Attention Mechanisms. In an interview, Ilya Sutskever, now the research director of OpenAI, mentioned that Attention Mechanisms are one of the most exciting advancements, and that they are here to stay. That sounds exciting. But what are Attention Mechanisms?

Attention Mechanisms in Neural Networks are (very) loosely based on the visual attention mechanism found in humans. Human visual attention is well-studied and while there exist different models, all of them essentially come down to being able to focus on a certain region of an image with “high resolution” while perceiving the surrounding image in “low resolution”, and then adjusting the focal point over time.

## Implementing a CNN for Text Classification in TensorFlow

The full code is available on Github.

In this post we will implement a model similar to Kim Yoon’s Convolutional Neural Networks for Sentence Classification. The model presented in the paper achieves good classification performance across a range of text classification tasks (like Sentiment Analysis) and has since become a standard baseline for new text classification architectures.