CUNI Submission to MRL 2023 Shared Task on Multi-lingual Multi-task Information Retrieval

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10476079" target="_blank" >RIV/00216208:11320/23:10476079 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

CUNI Submission to MRL 2023 Shared Task on Multi-lingual Multi-task Information Retrieval

Popis výsledku v původním jazyce

We present the Charles University system for the MRL 2023 Shared Task on Multi-lingual Multi-task Information Retrieval.The goal of the shared task was to develop systems for named entity recognition and question answering in several under-represented languages.Our solutions to both subtasks rely on the translate-test approach.We first translate the unlabeled examples into English using a multilingual machine translation model.Then, we run inference on the translated data using a strong task-specific model.Finally, we project the labeled data back into the original language.To keep the inferred tags on the correct positions in the original language, we propose a method based on scoring the candidate positions using a label-sensitive translation model.In both settings, we experiment with finetuning the classification models on the translated data.However, due to a domain mismatch between the development data and the shared task validation and test sets, the finetuned models could not outperform our baselines.

Název v anglickém jazyce

CUNI Submission to MRL 2023 Shared Task on Multi-lingual Multi-task Information Retrieval

Popis výsledku anglicky

We present the Charles University system for the MRL 2023 Shared Task on Multi-lingual Multi-task Information Retrieval.The goal of the shared task was to develop systems for named entity recognition and question answering in several under-represented languages.Our solutions to both subtasks rely on the translate-test approach.We first translate the unlabeled examples into English using a multilingual machine translation model.Then, we run inference on the translated data using a strong task-specific model.Finally, we project the labeled data back into the original language.To keep the inferred tags on the correct positions in the original language, we propose a method based on scoring the candidate positions using a label-sensitive translation model.In both settings, we experiment with finetuning the classification models on the translated data.However, due to a domain mismatch between the development data and the shared task validation and test sets, the finetuned models could not outperform our baselines.

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

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the The 2nd Workshop on Multi-lingual Representation Learning (MRL)

ISBN

979-8-89176-056-1

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

302-309

Název nakladatele

Association for Computational Linguistics

Místo vydání

Stroudsburg, PA, USA

Místo konání akce

Singapore, Singapore

Datum konání akce

7. 12. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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