An Empirical Study of Information Retrieval and Machine Reading Comprehension Algorithms for an Online Education Platform
Year: 2021
Type: conference-paper
Abstract: This paper provides an empirical study of various techniques for information retrieval and machine reading comprehension in the context of an online education platform. More specifically, our application deals with answering conceptual students questions on technology courses. To that end we explore a pipeline consisting of a document retriever and a document reader. We find that using TF-IDF document representations for retrieving documents and RoBERTa deep learning model for reading documents and answering questions yields the best performance with respect to F-Score. In overall, without a fine-tuning step, deep learning models have a significant performance gap with comparison to previously reported F-scores on other datasets.
Institutions: Universidade Federal do Ceará, Universidade Estadual do Ceará
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W4200225523