A data processing system implements obtaining textual content in a first language from a first client device and segmenting the textual content into a plurality of first tokens. The system also implements translating the first tokens from the first language to a second language using a bilingual dictionary, extracting features information from the second tokens to create a features vector, providing the feature vector to a first natural language processing model trained to analyze textual input in the second language and to output contextual information indicating one or more topics or subject matter of the first textual content, and providing the contextual information to a first machine learning model configured to analyze the contextual information and to identify one or more content items predicted to be relevant to the contextual information. The system further implements providing the information identifying the one or more content items to the first client device.

Described herein is an Autonomous Conversational AI system, which does not require any human configuration or annotation, and is used to have multi-tum dialogs with a user. A typical Conversational AI system consists of three main models: Natural Language Understanding (NLU), Dialog Manager (DM) and Natural Language Generation (NLG), which requires human provided data and configuration. The system proposed herein leverages novel Conversational AI methods which automatically generates conversational AI configuration from any historical conversation logs. The automatically generated configuration contains Auto-Topics, Auto-Subtopics, Auto-Intents, Auto-Responses and Auto-Flows which are used to automatically train NLU, DM and NLG models. Once these models are trained for given conversation logs, the system can be used to have dialog with any user.

Described herein is an Autonomous Conversational AI system, which does not require any human configuration or annotation, and is used to have multi-tum dialogs with a user. A typical Conversational AI system consists of three main models: Natural Language Understanding (NLU), Dialog Manager (DM) and Natural Language Generation (NLG), which requires human provided data and configuration. The system proposed herein leverages novel Conversational AI methods which automatically generates conversational AI configuration from any historical conversation logs. The automatically generated configuration contains Auto-Topics, Auto-Subtopics, Auto-Intents, Auto-Responses and Auto-Flows which are used to automatically train NLU, DM and NLG models. Once these models are trained for given conversation logs, the system can be used to have dialog with any user.

A method is provided for extracting machine readable data structures from unstructured, low-resource language input text. The method includes obtaining a corpus of high-resource language data structures, filtering the corpus of high-resource language data structures to obtain a filtered corpus of high-resource language data structures, obtaining entity types for each entity of each filtered high-resource language data structure, performing type substitution for each obtained entity by replacing each entity with an entity of the same type to generate type substituted data structures, and replacing each entity with an equivalent a corresponding low-resource language data structure entity to generate code switched sentences. The method further includes generating an augmented data structure corpus, training a multi-head self-attention transformer model, and providing the unstructured low-resource language input text to the trained model to extract the machine readable data structures.

A method is provided for extracting machine readable data structures from unstructured, low-resource language input text. The method includes obtaining a corpus of high-resource language data structures, filtering the corpus of high-resource language data structures to obtain a filtered corpus of high-resource language data structures, obtaining entity types for each entity of each filtered high-resource language data structure, performing type substitution for each obtained entity by replacing each entity with an entity of the same type to generate type substituted data structures, and replacing each entity with an equivalent a corresponding low-resource language data structure entity to generate code switched sentences. The method further includes generating an augmented data structure corpus, training a multi-head self-attention transformer model, and providing the unstructured low-resource language input text to the trained model to extract the machine readable data structures.

An AI platform may receive a request for information on text. The text is processed through a text mining pipeline for dynamic attribute extraction. An engine determines entities in the text and utilizes the entities to determine a relationship pattern. The engine identifies a trigger by matching one of the entities with a predefined entity in a utility authority file, locates an entity in close proximity to the trigger, identifies a value or regular expression in close proximity to the trigger in the text, and creates a triplet containing the entity, the trigger, and the value or regular expression, the triplet representing the relationship pattern. The engine applies an action to the triplet, wherein the action comprises obtaining the value from the text or translating the regular expression. The engine attaches the value or a result from the translating to the entity as a dynamic attribute of the entity.

An AI platform may receive a request for information on text. The text is processed through a text mining pipeline for dynamic attribute extraction. An engine determines entities in the text and utilizes the entities to determine a relationship pattern. The engine identifies a trigger by matching one of the entities with a predefined entity in a utility authority file, locates an entity in close proximity to the trigger, identifies a value or regular expression in close proximity to the trigger in the text, and creates a triplet containing the entity, the trigger, and the value or regular expression, the triplet representing the relationship pattern. The engine applies an action to the triplet, wherein the action comprises obtaining the value from the text or translating the regular expression. The engine attaches the value or a result from the translating to the entity as a dynamic attribute of the entity.

Systems and methods for improving question-answering recall for complex, multi-sentence, convergent questions. More specifically, an autonomous agent accesses an initial answer that partly answers a question received from a user device. The agent represents the question and the initial answer as discourse trees. From the discourse trees, the agent identifies entities in the question that are not addressed by the answer. The agent forms an additional discourse tree from an additional resource such as a corpus of text. The additional discourse tree rhetorically connects a non-addressed entity with the answer. The agent designates this discourse tree as an imaginary discourse tree. When combined with the initial answer discourse tree, the imaginary discourse tree is used to generate an improved answer relative to existing solutions.

Systems and methods for improving question-answering recall for complex, multi-sentence, convergent questions. More specifically, an autonomous agent accesses an initial answer that partly answers a question received from a user device. The agent represents the question and the initial answer as discourse trees. From the discourse trees, the agent identifies entities in the question that are not addressed by the answer. The agent forms an additional discourse tree from an additional resource such as a corpus of text. The additional discourse tree rhetorically connects a non-addressed entity with the answer. The agent designates this discourse tree as an imaginary discourse tree. When combined with the initial answer discourse tree, the imaginary discourse tree is used to generate an improved answer relative to existing solutions.

A method and a server system for facilitating implementation of regulations by organizations is described. Regulations are sourced from data stores associated with a plurality of regulatory authorities to configure a corpus of regulations. A plurality of enriched regulations is generated from the corpus of regulations. A user is enabled to create a construct related to at least one of an organization and an industry associated with the user. The construct is indicative of corresponding functional constituents and relationships among the functional constituents. Search and discovery of one or more regulations applicable to the organization or the industry is facilitated. Subsequent to the discovery of the one or more regulations, a linking of clauses of a respective enriched regulation to at least one functional constituent of the construct is enabled for facilitating implementation of the one or more regulations applicable to the organization or the industry associated with the user.