Babylon Partners Limited

Francesco Moramarco

Nils Yannick Hammerla

Jack Flann

April Tuesday Shen

Olufemi Awomosu

Pietro Cavallo

Aleksandar Savkov

Vitalii Zhelezniak

Alexsandar Savkov

Maria Lehl

Methods for determining whether two sets of words are similar are provided. In one aspect, a method includes receiving a first set of words and a second set of words, whichare subsets of a vocabulary, and each of the first and second sets of words include word embeddings corresponding to each word. The method also includes determining a word membership function for each word in the vocabulary. Determining the word membership includes determining a set of similarity values, each representing the similarity between the word and a respective word in the vocabulary. The method also includes determining a membership function for the first and second sets of words based on the determined word membership functions, and determining a set-based coefficient for the similarity between the first and second sets of words based on the membership function. Systems and devices are also provided.

Device and method for natural language processing

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 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 semantic graph of the input. This allows the methods described herein to function more efficiently and over a wider variety of potential inputs.

Semantic graph traversal for recognition of inferred clauses within natural language inputs

The disclosed system addresses a technical problem tied to computer technology and arising in the realm of computer memory capacity, namely the technical problem of providing a flexible response dialogue system that can be utilised for a variety of different types of dialogue without requiring the system to be specifically trained for each situation. This therefore avoids the need for large amounts of labelled training data for each type of dialogue (each potential conversation flow or subject area for the conversation). The disclosed system solves this technical problem by using semantic similarity to match a user's input to one of a set of predefined inputs (predefined user responses). Various mechanisms are implemented to provide disambiguation in the event of multiple potential matches for the input. By using semantic similarity, the user's response in unconstrained. This therefore provides a user interface that is more user-friendly.

Flexible-response dialogue system through analysis of semantic textual similarity

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.

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London, United Kingdom

Francesco Moramarco