厘清NLU领域中'假设'的多重定义，助力机器理解学术文本。
What Are Research Hypotheses?
- 梳理自然语言理解中'假设'的多种定义
- 揭示近年NLU任务对假设的不同诠释
- 为可机器解读的学术记录提供基础
过去几十年来，随着自然语言处理技术的发展，研究者们日益关注如何自动提取、理解、验证和生成开放领域及科学领域的假设。然而，'假设'这一术语在不同自然语言理解（NLU）任务中的解释已偏离传统自然科学、社会科学与形式科学中的定义。甚至在NLU领域内部，关于假设的界定也存在差异。本文综述并厘清了各类关于假设的定义，尤其关注近期发表的NLU任务中定义的细微差别。强调了结构良好、定义清晰的假设的重要性，尤其是在迈向机器可读学术记录的过程中。
原文摘要 · Abstract (English)
Over the past decades, alongside advancements in natural language processing, significant attention has been paid to training models to automatically extract, understand, test, and generate hypotheses in open and scientific domains. However, interpretations of the term \emph{hypothesis} for various natural language understanding (NLU) tasks have migrated from traditional definitions in the natural, social, and formal sciences. Even within NLU, we observe differences defining hypotheses across literature. In this paper, we overview and delineate various definitions of hypothesis. Especially, we discern the nuances of definitions across recently published NLU tasks. We highlight the importance of well-structured and well-defined hypotheses, particularly as we move toward a machine-interpretable scholarly record.
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