Forecast Utterance

Forecast utterance research centers on enabling natural language interaction with forecasting systems, aiming to bridge the gap between user needs and complex machine learning models. Current efforts focus on developing methods to accurately interpret user requests for predictions, often employing techniques like entity extraction and question-answering to extract relevant information from conversational input and subsequently select appropriate forecasting models (e.g., Transformers, recurrent neural networks, graph neural networks). This field is significant because it promises to democratize access to forecasting capabilities, improving decision-making across diverse domains by making sophisticated prediction tools more user-friendly and accessible to non-experts.