@inproceedings{yimam-etal-2026-saint,
title = "{SAINT}: Multilingual Span-Level Interpretability for Sentiment Analysis",
author = "Yimam, Seid Muhie and
Belay, Tadesse Destaw and
Geislinger, Robert and
Muhammad, Shamsuddeen Hassan and
Ohuoba, Adaeze Ngozi and
Imam, Sukairaj Hafiz and
Ayele, Abinew Ali and
Semmann, Martin and
Biemann, Chris and
Sharoff, Serge",
editor = "Ojha, Atul Kr. and
Sakti, Sakriani and
Soria, Claudia and
Melero, Maite and
McCrae, John P. and
Lignos, Constantine and
Liu, Chao-Hong and
Claramunt, German Rigau and
Rehm, Georg",
booktitle = "Proceedings of the {SIGUL} 2026 Joint Workshop with {ELE}, {EURALI}, and {DCLRL}: Towards Inclusivity and Equality: Language Resources and Technologies for Under-Resourced and Endangered Languages",
month = may,
year = "2026",
address = "Palma, Mallorca, Spain",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.sigul-1.6/",
doi = "10.63317/2ph93cb2qi57",
pages = "54--65",
abstract = "We investigate multilingual sentiment analysis and interpretability across high- and low-resource languages, focusing on Amharic, English, German, and Hausa. Our study evaluates encoder-only transformer models for both sequence-level sentiment classification and token-level attribution using Captum. Additionally, we assess zero- and few-shot decoder-only models for sequence-level sentiment prediction. Our results show that few-shot decoder-only models outperform encoder-only models on token-level sentiment classification in most languages, with the exception of Hausa, where a multilingual encoder-based model leads. For sequence-level sentiment classification, encoder-only models generally achieve strong performance across most languages, but decoder-only models are highly competitive, and may even surpass encoders, in the high-resource settings (German, English) and low-resource scenarios, depending on the prompting strategy. These findings highlight the utility of combining fine-tuned transformer models with prompt- based large language models to build interpretable sentiment analysis systems across both low- and high-resource languages. The SAINT dataset, annotation guideline, and evaluation scripts can be found at \url{https://github.com/uhh-hcds/SAINT}."
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<abstract>We investigate multilingual sentiment analysis and interpretability across high- and low-resource languages, focusing on Amharic, English, German, and Hausa. Our study evaluates encoder-only transformer models for both sequence-level sentiment classification and token-level attribution using Captum. Additionally, we assess zero- and few-shot decoder-only models for sequence-level sentiment prediction. Our results show that few-shot decoder-only models outperform encoder-only models on token-level sentiment classification in most languages, with the exception of Hausa, where a multilingual encoder-based model leads. For sequence-level sentiment classification, encoder-only models generally achieve strong performance across most languages, but decoder-only models are highly competitive, and may even surpass encoders, in the high-resource settings (German, English) and low-resource scenarios, depending on the prompting strategy. These findings highlight the utility of combining fine-tuned transformer models with prompt- based large language models to build interpretable sentiment analysis systems across both low- and high-resource languages. The SAINT dataset, annotation guideline, and evaluation scripts can be found at https://github.com/uhh-hcds/SAINT.</abstract>
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%0 Conference Proceedings
%T SAINT: Multilingual Span-Level Interpretability for Sentiment Analysis
%A Yimam, Seid Muhie
%A Belay, Tadesse Destaw
%A Geislinger, Robert
%A Muhammad, Shamsuddeen Hassan
%A Ohuoba, Adaeze Ngozi
%A Imam, Sukairaj Hafiz
%A Ayele, Abinew Ali
%A Semmann, Martin
%A Biemann, Chris
%A Sharoff, Serge
%Y Ojha, Atul Kr.
%Y Sakti, Sakriani
%Y Soria, Claudia
%Y Melero, Maite
%Y McCrae, John P.
%Y Lignos, Constantine
%Y Liu, Chao-Hong
%Y Claramunt, German Rigau
%Y Rehm, Georg
%S Proceedings of the SIGUL 2026 Joint Workshop with ELE, EURALI, and DCLRL: Towards Inclusivity and Equality: Language Resources and Technologies for Under-Resourced and Endangered Languages
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca, Spain
%F yimam-etal-2026-saint
%X We investigate multilingual sentiment analysis and interpretability across high- and low-resource languages, focusing on Amharic, English, German, and Hausa. Our study evaluates encoder-only transformer models for both sequence-level sentiment classification and token-level attribution using Captum. Additionally, we assess zero- and few-shot decoder-only models for sequence-level sentiment prediction. Our results show that few-shot decoder-only models outperform encoder-only models on token-level sentiment classification in most languages, with the exception of Hausa, where a multilingual encoder-based model leads. For sequence-level sentiment classification, encoder-only models generally achieve strong performance across most languages, but decoder-only models are highly competitive, and may even surpass encoders, in the high-resource settings (German, English) and low-resource scenarios, depending on the prompting strategy. These findings highlight the utility of combining fine-tuned transformer models with prompt- based large language models to build interpretable sentiment analysis systems across both low- and high-resource languages. The SAINT dataset, annotation guideline, and evaluation scripts can be found at https://github.com/uhh-hcds/SAINT.
%R 10.63317/2ph93cb2qi57
%U https://aclanthology.org/2026.sigul-1.6/
%U https://doi.org/10.63317/2ph93cb2qi57
%P 54-65
Markdown (Informal)
[SAINT: Multilingual Span-Level Interpretability for Sentiment Analysis](https://aclanthology.org/2026.sigul-1.6/) (Yimam et al., SIGUL-EURALI-DCLRL 2026)
ACL
- Seid Muhie Yimam, Tadesse Destaw Belay, Robert Geislinger, Shamsuddeen Hassan Muhammad, Adaeze Ngozi Ohuoba, Sukairaj Hafiz Imam, Abinew Ali Ayele, Martin Semmann, Chris Biemann, and Serge Sharoff. 2026. SAINT: Multilingual Span-Level Interpretability for Sentiment Analysis. In Proceedings of the SIGUL 2026 Joint Workshop with ELE, EURALI, and DCLRL: Towards Inclusivity and Equality: Language Resources and Technologies for Under-Resourced and Endangered Languages, pages 54–65, Palma, Mallorca, Spain. ELRA Language Resources Association (ELRA).