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 […]

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 […]

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 […]

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 […]