Implicit Word Reordering with Knowledge Distillation for Cross-Lingual Dependency Parsing

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AZQ4XFQBU" target="_blank" >RIV/00216208:11320/26:ZQ4XFQBU - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1609/aaai.v39i23.34632" target="_blank" >http://dx.doi.org/10.1609/aaai.v39i23.34632</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1609/aaai.v39i23.34632" target="_blank" >10.1609/aaai.v39i23.34632</a>

Result language

angličtina

Original language name

Implicit Word Reordering with Knowledge Distillation for Cross-Lingual Dependency Parsing

Original language description

Word order difference between source and target languages is a major obstacle to cross-lingual transfer, especially in the dependency parsing task. Current works are mostly based on order-agnostic models or word reordering to mitigate this problem. However, such methods either do not leverage grammatical information naturally contained in word order or are computationally expensive as the permutation space grows exponentially with the sentence length. Moreover, the reordered source sentence with an unnatural word order may be a form of noising that harms the model learning. To this end, we propose an Implicit Word Reordering framework with Knowledge Distillation (IWR-KD). This framework is inspired by that deep networks are good at learning feature linearization corresponding to meaningful data transformation, e.g. word reordering. To realize this idea, we introduce a knowledge distillation framework composed of a word-reordering teacher model and a dependency parsing student model. We verify our proposed method on Universal Dependency Treebanks across 31 different languages and show it outperforms a series of competitors, together with experimental analysis to illustrate how our method works towards training a robust parser. Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Czech name

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Czech description

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Type

D - Article in proceedings

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

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Continuities

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Publication year

2025

Confidentiality

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

Article name in the collection

Proc. AAAI Conf. Artif. Intell.

ISBN

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ISSN

21595399

e-ISSN

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Number of pages

9

Pages from-to

24530-24538

Publisher name

Association for the Advancement of Artificial Intelligence

Place of publication

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Event location

Philadelphia

Event date

Jan 1, 2026

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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