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.

通过自然对话，智能代理能够协助用户进行预测任务，而无需深入了解机器学习过程，本文通过引入一种名为预测话语的新概念并聚焦于从这些话语中准确解读用户预测目标，将任务作为一个槽填充问题，然后采用两种零样本方法解决槽填充任务，即实体抽取和问答技术，我们通过三个经过精心构建的数据集进行实验证明了我们雄心勃勃的目标的可行性，并展示了实体抽取和问答技术在解读预测话语中的有效性。