Conversational AI components; source: Rasa

Building an AI assistant is hard. Building an AI assistant that not only handles questions and executes tasks, but engages in a flexible back-and-forth dialogue can be tremendously difficult. It requires machine learning, engineering best practices, powerful tools, and data in the form of valuable user conversations.

You might think that building great AI assistants means building everything from scratch. But you don’t need to start with the basic building blocks to achieve something that’s performant, enterprise-ready, and flexible enough to fit your needs. …


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…


Photo by Alex Knight on Unsplash

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…


Photo by Alex Knight on Unsplash

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…

Mady Mantha

Conversational AI evangelist, Machine learning engineer, #NLProc, machine translation

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