UWB at WASSA-2024 Shared Task 2: Cross-lingual Emotion Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43972803" target="_blank" >RIV/49777513:23520/24:43972803 - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2024.wassa-1.47/" target="_blank" >https://aclanthology.org/2024.wassa-1.47/</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

UWB at WASSA-2024 Shared Task 2: Cross-lingual Emotion Detection

Popis výsledku v původním jazyce

This paper presents our system built for the WASSA-2024 Cross-lingual Emotion Detection Shared Task. The task consists of two subtasks: first, to assess an emotion label from six possible classes for a given tweet in one of five languages, and second, to predict words triggering the detected emotions in binary and numerical formats. Our proposed approach revolves around fine-tuning quantized large language models, specifically Orca 2, with low-rank adapters (LoRA) and multilingual Transformer-based models, such as XLM-R and mT5. We enhance performance through machine translation for both subtasks and trigger word switching for the second subtask. The system achieves excellent performance, ranking 1st in numerical trigger words detection, 3rd in binary trigger words detection, and 7th in emotion detection.

Název v anglickém jazyce

UWB at WASSA-2024 Shared Task 2: Cross-lingual Emotion Detection

Popis výsledku anglicky

This paper presents our system built for the WASSA-2024 Cross-lingual Emotion Detection Shared Task. The task consists of two subtasks: first, to assess an emotion label from six possible classes for a given tweet in one of five languages, and second, to predict words triggering the detected emotions in binary and numerical formats. Our proposed approach revolves around fine-tuning quantized large language models, specifically Orca 2, with low-rank adapters (LoRA) and multilingual Transformer-based models, such as XLM-R and mT5. We enhance performance through machine translation for both subtasks and trigger word switching for the second subtask. The system achieves excellent performance, ranking 1st in numerical trigger words detection, 3rd in binary trigger words detection, and 7th in emotion detection.

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

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, &amp; Social Media Analysis

ISBN

979-8-89176-156-8

ISSN

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e-ISSN

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Počet stran výsledku

7

Strana od-do

483-489

Název nakladatele

Association for Computational Linguistics

Místo vydání

Kerrville

Místo konání akce

Bangkok, Thailand

Datum konání akce

15. 8. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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