Enhancing Cross-Lingual Sarcasm Detection by a Prompt Learning Framework with Data Augmentation and Contrastive Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3ABRAGIEAZ" target="_blank" >RIV/00216208:11320/25:BRAGIEAZ - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195852507&doi=10.3390%2felectronics13112163&partnerID=40&md5=acd45b1c79d788cbdaa5cebc3f475c67" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195852507&doi=10.3390%2felectronics13112163&partnerID=40&md5=acd45b1c79d788cbdaa5cebc3f475c67</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/electronics13112163" target="_blank" >10.3390/electronics13112163</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing Cross-Lingual Sarcasm Detection by a Prompt Learning Framework with Data Augmentation and Contrastive Learning

Popis výsledku v původním jazyce

Given their intricate nature and inherent ambiguity, sarcastic texts often mask deeper emotions, making it challenging to discern the genuine feelings behind the words. The proposal of the sarcasm detection task is to assist us with more accurately understanding the true intention of the speaker. Advanced methods, such as deep learning and neural networks, are widely used in the field of sarcasm detection. However, most research mainly focuses on sarcastic texts in English, as other languages lack corpora and annotated datasets. To address the challenge of low-resource languages in sarcasm detection tasks, a zero-shot cross-lingual transfer learning method is proposed in this paper. The proposed approach is based on prompt learning and aims to assist the model with understanding downstream tasks through prompts. Specifically, the model uses prompt templates to construct training data into cloze-style questions and then trains them using a pre-trained cross-lingual language model. Combining data augmentation and contrastive learning can further improve the capacity of the model for cross-lingual transfer learning. To evaluate the performance of the proposed model, we utilize a publicly accessible sarcasm dataset in English as training data in a zero-shot cross-lingual setting. When tested with Chinese as the target language for transfer, our model achieves F1-scores of 72.14% and 76.7% on two test datasets, outperforming the strong baselines by significant margins. © 2024 by the authors.

Název v anglickém jazyce

Enhancing Cross-Lingual Sarcasm Detection by a Prompt Learning Framework with Data Augmentation and Contrastive Learning

Popis výsledku anglicky

Given their intricate nature and inherent ambiguity, sarcastic texts often mask deeper emotions, making it challenging to discern the genuine feelings behind the words. The proposal of the sarcasm detection task is to assist us with more accurately understanding the true intention of the speaker. Advanced methods, such as deep learning and neural networks, are widely used in the field of sarcasm detection. However, most research mainly focuses on sarcastic texts in English, as other languages lack corpora and annotated datasets. To address the challenge of low-resource languages in sarcasm detection tasks, a zero-shot cross-lingual transfer learning method is proposed in this paper. The proposed approach is based on prompt learning and aims to assist the model with understanding downstream tasks through prompts. Specifically, the model uses prompt templates to construct training data into cloze-style questions and then trains them using a pre-trained cross-lingual language model. Combining data augmentation and contrastive learning can further improve the capacity of the model for cross-lingual transfer learning. To evaluate the performance of the proposed model, we utilize a publicly accessible sarcasm dataset in English as training data in a zero-shot cross-lingual setting. When tested with Chinese as the target language for transfer, our model achieves F1-scores of 72.14% and 76.7% on two test datasets, outperforming the strong baselines by significant margins. © 2024 by the authors.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

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

2024

Kód důvěrnosti údajů

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

Název periodika

Electronics (Switzerland)

ISSN

2079-9292

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

1-14

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85195852507