Abstract: This application provides systems and methods for training a language model to perform one or more specific natural language processing tasks. The embodiments described herein fine-tune language models for downstream tasks solely by pre-processing the training data set. Rather than fine-tuning via architecture changes (e.g., addition of classification layers on top of a language model), the embodiments described herein fine-tune language model(s) via dataset pre-processing alone. This is much simpler for the practitioner. Furthermore, it allows iterative additions of functionality to the language model without a complete restructure of the architecture. This is possible because of the general nature of the language-modelling task, which essentially consists of predicting what comes next in a sequence given some context. If training data can be framed in this manner, a language model can be used to solve that task directly without architecture modifications.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Computer-implemented methods for determining missing content in a database are provided. The database may contain a plurality of known embedded sentences and their relationship to content. In one aspect, a method includes receiving new queries and generating new embedded sentences from said new queries. The method also includes determining whether the new embedded sentences are similar to known embedded sentences. The method also includes generating a message indicating that new embedded sentence is not linked to content. Systems are also provided.

Abstract: 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.

Abstract: Computer-implemented methods for retrieving content in response to receiving a natural language query are provided. In one aspect, a method includes receiving a natural language query submitted by a user using a user interface, generating an embedded sentence from said query, determining a similarity between the embedded sentence derived from the received natural language query and embedded sentences from queries saved in a database comprising a fixed mapping of responses to saved queries expressed as the embedded sentences, retrieving a response for an embedded sentence determined to be similar to one of the saved queries, and providing the response to the user via the user interface. Systems are also provided.