Simple and Effective Graph-to-Graph Annotation Conversion

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AJIJR3EAS" target="_blank" >RIV/00216208:11320/22:JIJR3EAS - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2022.coling-1.484" target="_blank" >https://aclanthology.org/2022.coling-1.484</a>

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

Simple and Effective Graph-to-Graph Annotation Conversion

Original language description

Annotation conversion is an effective way to construct datasets under new annotation guidelines based on existing datasets with little human labour. Previous work has been limited in conversion between tree-structured datasets and mainly focused on feature-based models which are not easily applicable to new conversions. In this paper, we propose two simple and effective graph-to-graph annotation conversion approaches, namely Label Switching and Graph2Graph Linear Transformation, which use pseudo data and inherit parameters to guide graph conversions respectively. These methods are able to deal with conversion between graph-structured annotations and require no manually designed features. To verify their effectiveness, we manually construct a graph-structured parallel annotated dataset and evaluate the proposed approaches on it as well as other existing parallel annotated datasets. Experimental results show that the proposed approaches outperform strong baselines with higher conversion score. To further validate the quality of converted graphs, we utilize them to train the target parser and find graphs generated by our approaches lead to higher parsing score than those generated by the baselines.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

—

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 29th International Conference on Computational Linguistics

ISBN

—

ISSN

2951-2093

e-ISSN

—

Number of pages

11

Pages from-to

5450-5460

Publisher name

International Committee on Computational Linguistics

Place of publication

—

Event location

Gyeongju, Republic of Korea

Event date

Jan 1, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—