Relation/Entity-Centric Reading Comprehension

08/27/2020
by   Takeshi Onishi, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/16/2019

KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension

Machine Reading Comprehension (MRC) is a task that requires machine to u...
research
04/16/2021

ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoning

Stories and narratives are composed based on a variety of events. Unders...
research
10/12/2018

Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension

We propose a neural machine-reading model that constructs dynamic knowle...
research
02/19/2015

Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

One long-term goal of machine learning research is to produce methods th...
research
10/05/2018

Entity Tracking Improves Cloze-style Reading Comprehension

Reading comprehension tasks test the ability of models to process long-t...
research
11/23/2016

Emergent Predication Structure in Hidden State Vectors of Neural Readers

A significant number of neural architectures for reading comprehension h...
research
04/06/2023

Evaluating the Robustness of Machine Reading Comprehension Models to Low Resource Entity Renaming

Question answering (QA) models have shown compelling results in the task...

Please sign up or login with your details

Forgot password? Click here to reset