Multi-features Enhanced Multi-task Learning for Vietnamese Treebank Conversion

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AXQXXWTRJ" target="_blank" >RIV/00216208:11320/26:XQXXWTRJ - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-981-97-8367-0_6" target="_blank" >http://dx.doi.org/10.1007/978-981-97-8367-0_6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-97-8367-0_6" target="_blank" >10.1007/978-981-97-8367-0_6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-features Enhanced Multi-task Learning for Vietnamese Treebank Conversion

Popis výsledku v původním jazyce

Pre-trained language representation-based dependency parsing models have achieved obvious improvements in rich-resource languages. However, these model performances depend on the quality and scale of training data significantly. Compared with Chinese and English, the scale of Vietnamese Dependency treebank is scarcity. Considering human annotation is labor-intensive and time-consuming, we propose a multi-features enhanced multi-task learning framework to convert all heterogeneous Vietnamese Treebanks to a unified one. On the one hand, we exploit Tree BiLSTM and pattern embedding to extract global and local dependency tree features from the source Treebank. On the other hand, we propose to integrate these features into a multi-task learning framework to use the source dependency parsing to assist the conversion processing. Experiments on the benchmark datasets show that our proposed model can effectively convert heterogeneous treebanks, thus further improving the Vietnamese dependency parsing accuracy by about 7.12 points in LAS. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Název v anglickém jazyce

Multi-features Enhanced Multi-task Learning for Vietnamese Treebank Conversion

Popis výsledku anglicky

Pre-trained language representation-based dependency parsing models have achieved obvious improvements in rich-resource languages. However, these model performances depend on the quality and scale of training data significantly. Compared with Chinese and English, the scale of Vietnamese Dependency treebank is scarcity. Considering human annotation is labor-intensive and time-consuming, we propose a multi-features enhanced multi-task learning framework to convert all heterogeneous Vietnamese Treebanks to a unified one. On the one hand, we exploit Tree BiLSTM and pattern embedding to extract global and local dependency tree features from the source Treebank. On the other hand, we propose to integrate these features into a multi-task learning framework to use the source dependency parsing to assist the conversion processing. Experiments on the benchmark datasets show that our proposed model can effectively convert heterogeneous treebanks, thus further improving the Vietnamese dependency parsing accuracy by about 7.12 points in LAS. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lect. Notes Comput. Sci.

ISBN

978-981-97-8366-3

ISSN

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e-ISSN

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Počet stran výsledku

15

Strana od-do

91-105

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

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Místo konání akce

Taiyuan

Datum konání akce

1. 1. 2026

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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