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Pregled bibliografske jedinice broj: 1121017

Multi-task learning for cross-lingual sentiment analysis


(MSC ITN CLEOPATRRA) Thakkar, Gaurish; Mikelić Preradović, Nives; Tadić, Marko
Multi-task learning for cross-lingual sentiment analysis // Proceedings of the 2nd International Workshop on Cross-lingual Event-centric Open Analytics / Demidova, Elena ; Hakimov, Sherzod ; Winters, Jane ; Tadić, Marko (ur.).
Ljubljana: CEUR Workshop Proceedings, 2021. str. 76-84 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 1121017 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Multi-task learning for cross-lingual sentiment analysis

Autori
Thakkar, Gaurish ; Mikelić Preradović, Nives ; Tadić, Marko

Kolaboracija
MSC ITN CLEOPATRRA

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the 2nd International Workshop on Cross-lingual Event-centric Open Analytics / Demidova, Elena ; Hakimov, Sherzod ; Winters, Jane ; Tadić, Marko - Ljubljana : CEUR Workshop Proceedings, 2021, 76-84

Skup
2nd International Workshop on Cross-lingual Event- centric Open Analytics

Mjesto i datum
Ljubljana, Slovenija, 12.04.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
sentiment analysis ; cross-lingual ; transfer learning ; multi-task learning ; news sentiment ; under-resourced languages

Sažetak
This paper presents a cross-lingual sentiment analysis of news articles using zero-shot and few-shot learning. The study aims to classify the Croatian news articles with the positive, negative, and neu- tral sentiment using the Slovene dataset. The system is based on a trilin- gual BERT-based model trained in three languages: English, Slovene, Croatian. The paper analyses different setups of using datasets in two languages and proposes a simple multi-task model to perform sentiment classification. The evaluation is performed using the few-shot and zero- shot scenarios in single-task and multi-task experiments for Croatian and Slovene.

Izvorni jezik
Engleski

Znanstvena područja
Informacijske i komunikacijske znanosti, Filologija



POVEZANOST RADA


Projekti:
EK-H2020-812997 - Cross-lingual Event-centric Open Analytics Research Academy (Cleopatra) (Tadić, Marko, EK - H2020-MSCA-ITN-2018) ( POIROT)

Ustanove:
Filozofski fakultet, Zagreb

Poveznice na cjeloviti tekst rada:

ceur-ws.org

Citiraj ovu publikaciju:

(MSC ITN CLEOPATRRA) Thakkar, Gaurish; Mikelić Preradović, Nives; Tadić, Marko
Multi-task learning for cross-lingual sentiment analysis // Proceedings of the 2nd International Workshop on Cross-lingual Event-centric Open Analytics / Demidova, Elena ; Hakimov, Sherzod ; Winters, Jane ; Tadić, Marko (ur.).
Ljubljana: CEUR Workshop Proceedings, 2021. str. 76-84 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
(MSC ITN CLEOPATRRA) (MSC ITN CLEOPATRRA) Thakkar, G., Mikelić Preradović, N. & Tadić, M. (2021) Multi-task learning for cross-lingual sentiment analysis. U: Demidova, E., Hakimov, S., Winters, J. & Tadić, M. (ur.)Proceedings of the 2nd International Workshop on Cross-lingual Event-centric Open Analytics.
@article{article, year = {2021}, pages = {76-84}, keywords = {sentiment analysis, cross-lingual, transfer learning, multi-task learning, news sentiment, under-resourced languages}, title = {Multi-task learning for cross-lingual sentiment analysis}, keyword = {sentiment analysis, cross-lingual, transfer learning, multi-task learning, news sentiment, under-resourced languages}, publisher = {CEUR Workshop Proceedings}, publisherplace = {Ljubljana, Slovenija} }
@article{article, year = {2021}, pages = {76-84}, keywords = {sentiment analysis, cross-lingual, transfer learning, multi-task learning, news sentiment, under-resourced languages}, title = {Multi-task learning for cross-lingual sentiment analysis}, keyword = {sentiment analysis, cross-lingual, transfer learning, multi-task learning, news sentiment, under-resourced languages}, publisher = {CEUR Workshop Proceedings}, publisherplace = {Ljubljana, Slovenija} }




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