Introducing "Forecast Utterance" for Conversational Data Science
Md Mahadi Hassan Alex Knipper Shubhra Kanti Karmaker
Envision an intelligent agent capable of assisting users in conducting
forecasting tasks through intuitive, natural conversations, without requiring
in-depth knowledge of the underlying machine learning (ML) processes. A
significant challenge for the agent in this endeavor is to accurately
comprehend the user's prediction goals and, consequently, formulate precise ML
tasks. In this paper, we take a pioneering step towards this ambitious goal by
introducing a new concept called Forecast Utterance and then focus on the
automatic and accurate interpretation of users' prediction goals from these
utterances. Specifically, we frame the task as a slot-filling problem, where
each slot corresponds to a specific aspect of the goal prediction task. We then
employ two zero-shot methods for solving the slot-filling task, namely: 1)
Entity Extraction (EE), and 2) Question-Answering (QA) techniques. Our
experiments, conducted with three meticulously crafted data sets, validate the
viability of our ambitious goal and demonstrate the effectiveness of both EE
and QA techniques in interpreting Forecast Utterances.