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Main Author: Xiong, Guanming
Format: Preprint
Published: 2020
Subjects:
Online Access:https://arxiv.org/abs/2004.13821
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author Xiong, Guanming
author_facet Xiong, Guanming
contents In this paper, we present a two stage model for multi-hop question answering. The first stage is a hierarchical graph network, which is used to reason over multi-hop question and is capable to capture different levels of granularity using the nature structure(i.e., paragraphs, questions, sentences and entities) of documents. The reasoning process is convert to node classify task(i.e., paragraph nodes and sentences nodes). The second stage is a language model fine-tuning task. In a word, stage one use graph neural network to select and concatenate support sentences as one paragraph, and stage two find the answer span in language model fine-tuning paradigm.
format Preprint
id arxiv_https___arxiv_org_abs_2004_13821
institution arXiv
publishDate 2020
record_format arxiv
spellingShingle Fine-tuning Multi-hop Question Answering with Hierarchical Graph Network
Xiong, Guanming
Computation and Language
Artificial Intelligence
Machine Learning
In this paper, we present a two stage model for multi-hop question answering. The first stage is a hierarchical graph network, which is used to reason over multi-hop question and is capable to capture different levels of granularity using the nature structure(i.e., paragraphs, questions, sentences and entities) of documents. The reasoning process is convert to node classify task(i.e., paragraph nodes and sentences nodes). The second stage is a language model fine-tuning task. In a word, stage one use graph neural network to select and concatenate support sentences as one paragraph, and stage two find the answer span in language model fine-tuning paradigm.
title Fine-tuning Multi-hop Question Answering with Hierarchical Graph Network
topic Computation and Language
Artificial Intelligence
Machine Learning
url https://arxiv.org/abs/2004.13821