TOPRO: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks

Result code in IS VaVaI

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Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189933264&partnerID=40&md5=6080c6b90adddc9c1614d55e5cc6f5f9" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189933264&partnerID=40&md5=6080c6b90adddc9c1614d55e5cc6f5f9</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

TOPRO: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks

Original language description

Prompt-based methods have been successfully applied to multilingual pretrained language models for zero-shot cross-lingual understanding. However, most previous studies primarily focused on sentence-level classification tasks, and only a few considered token-level labeling tasks such as Named Entity Recognition (NER) and Part-of-Speech (POS) tagging. In this paper, we propose Token-Level Prompt Decomposition (TOPRO), which facilitates the prompt-based method for token-level sequence labeling tasks. The TOPRO method decomposes an input sentence into single tokens and applies one prompt template to each token. Our experiments on multilingual NER and POS tagging datasets demonstrate that TOPRO-based fine-tuning outperforms Vanilla fine-tuning and Prompt-Tuning in zero-shot cross-lingual transfer, especially for languages that are typologically different from the source language English. Our method also attains state-of-the-art performance when employed with the mT5 model. Besides, our exploratory study in multilingual large language models shows that TOPRO performs much better than the current in-context learning method. Overall, the performance improvements show that TOPRO could potentially serve as a novel and simple benchmarking method for sequence labeling tasks. © 2024 Association for Computational Linguistics.

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

2024

Confidentiality

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

Article name in the collection

EACL - Conf. European Chapter Assoc. Comput. Linguist., Proc. Conf.

ISBN

979-889176088-2

ISSN

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e-ISSN

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Number of pages

18

Pages from-to

2685-2702

Publisher name

Association for Computational Linguistics (ACL)

Place of publication

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Event location

St. Julian's

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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