Prerequisites for Explainable Machine Reading Comprehension: A Position Paper.
Year: 2020
Type: preprint
Abstract: Machine reading comprehension (MRC) has received considerable attention in natural language processing over the past few years. However, the conventional task design of MRC lacks the explainability beyond the model interpretation, i.e., the internal mechanics of the model cannot be explained in human terms. To this end, this position paper provides a theoretical basis for the design of MRC based on psychology and psychometrics and summarizes it in terms of the requirements for explainable MRC. We conclude that future datasets should (i) evaluate the capability of the model for constructing a coherent and grounded representation to understand context-dependent situations and (ii) ensure substantive validity by improving the question quality and by formulating a white-box task.
Source: arXiv (Cornell University)
Authors: Saku Sugawara, Pontus Stenetorp, Akiko Aizawa
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3015031314