Interpreting Themes from Educational Stories
This work addresses the problem of advancing beyond literal comprehension in reading for NLP researchers, though it is incremental as it builds on existing MRC frameworks.
The authors tackled the problem of interpretive comprehension in NLP by focusing on inferring themes from educational narratives, introducing the first dataset for this task and finding it challenging and significant through experiments with state-of-the-art methods.
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.