# Relation/Entity-Centric Reading Comprehension

Source: [Relation/Entity-Centric Reading Comprehension](<https://www.sota2.com/research/paper/68fa68321e209131cea1ab0b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:44.201Z

Authors: Takeshi Onishi

Published: 2020-08-27T06:42:18.000Z

## Abstract

Constructing a machine that understands human language is one of the most elusive and long-standing challenges in artificial intelligence. This thesis addresses this challenge through studies of reading comprehension with a focus on understanding entities and their relationships. More specifically, we focus on question answering tasks designed to measure reading comprehension. We focus on entities and relations because they are typically used to represent the semantics of natural language.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2008.11940>)

[PDF](<https://arxiv.org/pdf/2008.11940.pdf>)