⚠️ Unsupported Browser

Your browser is not supported.

The latest version of Safari, Chrome, Firefox, Internet Explorer or Microsoft Edge is required to use this website.

Click the button below to update and we look forward to seeing you soon.

Update now

All Posts

Modular Intent Design: A More Effective Way of Understanding Language

NLU for Dialogue is a Unique Machine Learning Problem Consider speech recognition. This is a machine learning problem that also requires a lot of data. This data can be annotated relatively simply by a person listening to a recording and transcribing what is said.  With machine translation, there are many different ways to annotate data […]

PolyAI’s ConveRT Model Outperforms BERT and GPT-Based Models in Salesforce Research Evaluation

In a recent evaluation by Salesforce Research, PolyAI’s ConveRT model performed top across a range of metrics, while using a fraction of the computational resources. Salesforce’s recent paper, Probing Task-Oriented Dialogue Representation from Language Models, compared ConveRT to other pre-trained models, evaluating their ability to encapsulate conversational knowledge in application to Conversational AI tasks. ConveRT […]

ConVEx: How PolyAI created the most accurate value extractor on the market

We’re thrilled to announce our recently published paper on the PolyAI ConVEx framework. Our new technique, ConVEx (Conversational Value Extractor), is the most accurate value extractor on the market. It requires significantly less data than previous best systems, which means that PolyAI can create virtual assistants faster and better than anyone else, across any customer […]

Intent Classification with Geometrically-Friendly Embeddings

At PolyAI, our conversational agents are powered, in part, by machine learning models that detect the intent behind what a user says. For example, in a banking environment, if a customer says “When did you send me my new card?”, the models will detect that you’re enquiring about card arrivals and the agent will route […]