Interpreting Themes from Educational Stories
Yigeng Zhang Fabio A. González Thamar Solorio
Reading comprehension continues to be a crucial research focus in the NLP
community. Recent advances in Machine Reading Comprehension (MRC) have mostly
centered on literal comprehension, referring to the surface-level understanding
of content. In this work, we focus on the next level - interpretive
comprehension, with a particular emphasis on inferring the themes of a
narrative text. We introduce the first dataset specifically designed for
interpretive comprehension of educational narratives, providing corresponding
well-edited theme texts. The dataset spans a variety of genres and cultural
origins and includes human-annotated theme keywords with varying levels of
granularity. We further formulate NLP tasks under different abstractions of
interpretive comprehension toward the main idea of a story. After conducting
extensive experiments with state-of-the-art methods, we found the task to be
both challenging and significant for NLP research. The dataset and source code
have been made publicly available to the research community at
https://github.com/RiTUAL-UH/EduStory.