Abstractions for narrative comprehension tasks

This paper presents ongoing work in investigating the scale at which semantic abstractions are useful in intelligent reasoning about narrative. One method of evaluating narrative reasoning is to use comprehension tests on stories based on question-answering. Recent advances in language processing have led to promising results in general question-answering. However, current systems fail to accurately answer questions when information is not explicitly mentioned in the input story. Specifically, we are interested in testing whether corpus-based deep learning methods can be extended with classical logic-based approaches to draw inferences beyond ones that are explicitly mentioned in sentences on the corpus. This paper describes a preliminary reimplementation of current methods on the bAbI corpus for question-answering and then presents an algorithm for reasoning about missing information in the input by removing sentences from the corpus.

Duke Scholars

Author Arnav Jhala Engineering Graduate and Professional Programs