Exploring Semantic Map Embeddings / Part II
In this second part of our series on semantic maps, we show how to create them and see how they perform as featurizers for DIET.…
Exploring Semantic Map Embeddings / Part I
We explore a new sparse text embedding that has some interesting properties.…
Introducing Entity Roles and Groups
At a fundamental level, natural language understanding (NLU) does two things: it identifies the goal or meaning of the text and extracts key pieces of information…
10 Best Practices for Designing NLU Training Data
Whether you're starting from scratch or working with an existing data set, here's how to make sure your NLU training data results in accurate predictions and scales sustainably.…
Introducing DIET: state-of-the-art architecture that outperforms fine-tuning BERT and is 6X faster to train
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).…