Cross-lingual transfer learning for relation extraction using Universal Dependencies

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A6TSMFV6D" target="_blank" >RIV/00216208:11320/22:6TSMFV6D - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0885230821000711" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0885230821000711</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.csl.2021.101265" target="_blank" >10.1016/j.csl.2021.101265</a>

Result language

angličtina

Original language name

Cross-lingual transfer learning for relation extraction using Universal Dependencies

Original language description

This paper focuses on the task of cross-language relation extraction, which aims to identify the semantic relations holding between entities in the text. The goal of the task is to train classifiers for low-resource languages by means of the annotated data from high-resource languages. Related methods usually employ parallel data or Machine Translator (MT) to project annotated data from a source to a target language. However, the availability and the quality of parallel data and MT are big challenges for low-resource languages. In this paper, a novel transfer learning method is presented for this task. The key idea is to utilize a tree-based representation of data, which is highly informative for classifying semantic relations, and also shared among different languages. All the training and test data are shown using this representation. We propose to use the Universal Dependency (UD) parsing, which is a language-agnostic formalism for representation of syntactic structures. Equipping UD parse trees with multi-lingual word embeddings makes an ideal representation for the cross-language relation extraction task. We propose two deep networks to use this representation. The first one utilizes the Shortest Dependency Path of UD trees, while the second employs the UD-based positional embeddings. Experiments are performed using SemEval 2010-task 8 training data, whereas French and Farsi are the test languages. The results show 63.9% and 56.2% F1 scores, for French and Farsi test data, respectively, which are 14.4% and 17.9% higher than the baseline. This work can be considered a simple yet powerful baseline for further investigation into the cross-language tasks.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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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

Name of the periodical

Computer Speech and Language

ISSN

0885-2308

e-ISSN

1095-8363

Volume of the periodical

71

Issue of the periodical within the volume

2022-1-1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

12

Pages from-to

1-12

UT code for WoS article

000761599000006

EID of the result in the Scopus database

2-s2.0-85111004456