mlsanityData to 14 Oct 2026

Introducing "Forecast Utterance" for Conversational Data Science

arXiv 2309.03877 ↗First posted 7 Sep 2023cs.CL cs.HCcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 41 of its 78 references that are in the map. First posted on arXiv 7 Sep 2023.

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41 of 78 references.

References · 41

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29 more
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