At Rasa, we’re excited about making cutting-edge machine learning technology accessible in a developer-friendly workflow. With Rasa 1.8, our research team is releasing a new state-of-the-art lightweight, multitask transformer architecture for NLU: Dual Intent and Entity Transformer (DIET).

In this post, we’ll talk about DIET’s features and how you can use it in Rasa to achieve more accuracy than anything we had before. We’re releasing an academic paper that demonstrates that this new architecture improves upon the current state of the art, outperforms fine-tuning BERT, and is six times faster to train.

What is DIET

DIET is a multi-task transformer architecture that handles…

In the first part of this series, we introduced the different maturity levels of conversational AI and started building a travel assistant using Rasa. In this post, we’ll look at structuring happy and unhappy conversation paths, various machine learning policies and configurations to improve your dialogue model, and use a transfer learning based language model to generate natural conversations.

Rasa recently released version 1.0 in which they combined Core and NLU into a single package. We’ll be using Rasa 1.0 in this article.

What can you do, Coop?

Since the primary purpose of the assistant, let’s name it Coop, is to book awesome vacations, Coop…

Though Conversational AI has been around since the 1960s, it’s experiencing a renewed focus in recent years. While we’re still in the early days of the design and development of intelligent conversational AI, Google quite rightly announced that we were moving from a mobile-first to an AI- first world, where we expect technology to be naturally conversational, thoughtfully contextual, and evolutionarily competent. In other words, we expect technology to learn and evolve.

Most chatbots today can handle simple questions and respond with prebuilt responses based on rule-based conversation processing. For instance, if user says X, respond with Y; if user…