Year: 2018
Type: conference-paper
Abstract: In recent years many deep neural networks have been proposed to solve Reading Comprehension (RC) tasks.Most of these models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span in a given document.We present a novel neural-based architecture that is capable of extracting relevant regions based on a given question-document pair and generating a well-formed answer.To show the effectiveness of our architecture, we conducted several experiments on the recently proposed and challenging RC dataset 'Nar-rativeQA'.The proposed architecture outperforms state-of-the-art results (Tay et al., 2018) by 12.62% (ROUGE-L) relative improvement.
DOI: 10.18653/v1/d18-1054
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2890498499