Abstract: One or more unit-test cases are generated from a monolingual code corpus and the generated unit-test cases are filtered to generate a corpus of unit-test cases which have acceptability scores exceeding one or more predefined thresholds. One or more of the code samples of the monolingual code corpus are translated from a source language to a target language using a pretrained Large Language Model and the generated unit-test cases are translated from the source language to the target language. The LLM-translated code samples are validated using the translated unit-test cases and a parallel-data training corpus comprising the LLM-translated code samples that pass the validation is created. The pretrained large language model (LLM) is fine-tuned using the parallel-data training corpus, a given code segment is translated using the fine-tuned large language model (LLM), the translated given code segment is tested and the tested given code segment is deployed.

Abstract: An embodiment for generating multi-turn dialog datasets for training of dialog or conversational agents. The embodiment may select an agent from a set of agents. The embodiment may automatically identify sentences from training data of the selected agent that satisfy a first sequential node condition of the selected random dialog node. The embodiment may automatically determine an approach for responding to the first sequential node condition of the selected random dialog node that either satisfies the first sequential dialog node condition, or inserts a multi-turn conversational property, and generate a corresponding response. The embodiment may automatically determine additional approaches for responding to each condition within subsequent sequential child nodes of the selected random dialog node that either satisfy each subsequent sequential child node condition or insert a multi-turn conversational property, and generate corresponding responses.

Abstract: A data visualization method, system, and computer program product that includes identifying an intent from a natural language query in a conversation with a conversational system, utilizing verbiage from the natural language query and the intent to determine one or more data columns for visualization of results of the natural language query, and displaying a visualization of the determined one or more data columns.