Abstract: A method and a system for generating synthetic data for training a domain-specific model are provided. The method includes: accessing a first dataset that includes attributes associated with a domain of the domain-specific model; accessing templates in a predetermined format that include at least one variable; selecting a first textual template from among the at least one textual template; generating a synthetic dataset by substituting each respective variable of the first textual template with a respective attribute from the first dataset such that each respective attribute is arranged in a sequence defined by the first textual template; and training, the domain-specific model using the synthetic dataset. Each respective text string provides a natural language simulated example, and each respective corresponding label sequence provides contextual information to facilitate learning by the domain-specific model.

Abstract: Systems and methods for providing an archive of precedents are provided. The archive of precedents includes determining precedent data; parsing a new-event XML file for string data, wherein the string data is related to a new event; generating a vector embedding from the string data; determining a similar embedding in a vector space, wherein the similar embedding is a vector embedding of an instance of precedent data; parsing XML files of the precedent and new event and using parsed data to generate an old-to-new value map; retrieving the corresponding XML file and the corresponding precedent-event document associated with the vector embedding of the instance of precedent data; and providing, as input to a natural language processing model, the list of text strings recovered from the editable form of the document of the precedent event and the old-to-new value map.

Abstract: Systems and methods for providing an archive of precedents are provided. The archive of precedents includes determining precedent data; parsing a new-event XML file for string data, wherein the string data is related to a new event; generating a vector embedding from the string data; determining a similar embedding in a vector space, wherein the similar embedding is a vector embedding of an instance of precedent data; parsing XML files of the precedent and new event and using parsed data to generate an old-to-new value map; retrieving the corresponding XML file and the corresponding precedent-event document associated with the vector embedding of the instance of precedent data; and providing, as input to a natural language processing model, the list of text strings recovered from the editable form of the document of the precedent event and the old-to-new value map.

Abstract: Various methods and processes, apparatuses or systems, and media for data processing are disclosed. A processor implements an AI powered bot system; receives, via a user interface within the bot, user input as text data wherein the text data indicates a RFQ for a derivative instrument; transmits the text data to an AIML NLP service; extracts, by a parsing module, parameters associated with the RFQ by utilizing an entity extraction model provided by the AIML NLP service; normalizes the extracted parameters and transmits the normalized parameters to an automated pricing module; receives pricing details data by a response generation component embedded within the parsing module from the automated pricing module; and transmits the pricing details data to the bot for receiving user input via the user interface to conduct a transaction with respect to the derivative instrument.

Abstract: Systems and methods for providing an archive of precedents are provided. The archive of precedents includes determining precedent data; parsing a new-event XML file for string data, wherein the string data is related to a new event; generating a vector embedding from the string data; determining a similar embedding in a vector space, wherein the similar embedding is a vector embedding of an instance of precedent data; parsing XML files of the precedent and new event and using parsed data to generate an old-to-new value map; retrieving the corresponding XML file and the corresponding precedent-event document associated with the vector embedding of the instance of precedent data; and providing, as input to a natural language processing model, the list of text strings recovered from the editable form of the document of the precedent event and the old-to-new value map.