Babylon Partners Limited

Maria Lehl

Francesco Moramarco

Jack Flann

Olufemi Awomosu

April Tuesday Shen

Embodiments described herein provide a more flexible, effective, and computationally efficient means for determining multiple intents within a natural language input. Some methods rely on specifically trained machine learning classifiers to determine multiple intents within a natural language input. These classifiers typically require a large amount of labelled training data in order to work effectively, and are generally only applicable to determining specific types of intents (e.g., a specifically selected set of potential inputs). In contrast, the embodiments described herein avoid the use of specifically trained classifiers by determining inferred clauses from a syntactic graph of the input. This allows the methods described herein to function more efficiently and over a wider variety of potential inputs. Furthermore, by determining inferred clauses from a syntactic graph, rather than a semantic graph, the methods described herein avoid the additional computation and potential additional errors associated with the generation of a semantic graph from a syntactic graph.

Growing community of inventors

London, United Kingdom

Maria Lehl