Papers with low-resource language settings
Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages (2022.emnlp-main)
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| Challenge: | Hate speech datasets focus on English-language content, hindering effective models . annotating hateful content is expensive, time-consuming and potentially harmful to annotators. |
| Approach: | They propose to use ISO 639-1 codes to fine-tune models on one source language and apply them to another language. |
| Outcome: | The proposed approach performs well on some tasks, but fails on many others. |
A multi-level multi-label text classification dataset of 19th century Ottoman and Russian literary and critical texts (2024.findings-acl)
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| Challenge: | a multi-level, multi-label text classification dataset is used to classify over 3000 documents . authors use a classical bag-of-words (BoW) naive Bayes model and three modern LLMs . |
| Approach: | They propose to apply large language models to a multi-level, multi-label text classification dataset . the dataset features literary and critical texts from 19th-century Ottoman Turkish and Russian . |
| Outcome: | The proposed dataset features literary and critical texts from 19th-century Ottoman Turkish and Russian. |
A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are increasingly used to generate synthetic textual data for training smaller specialized models. |
| Approach: | They evaluate the performance of large language models and their generation strategies in 11 different languages using 3 NLP tasks and 4 open-source LLMs. |
| Outcome: | The proposed generation strategies and their combinations yield strong results across 11 languages, including several extremely low-resource ones. |
Parsing the Switch: LLM-Based UD Annotation for Complex Code-Switched and Low-Resource Languages (2025.findings-emnlp)
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| Challenge: | Existing approaches to analyzing code-switched data are limited in their ability to generalize to multilingual and mixed-language inputs. |
| Approach: | They propose a large-language model-based annotation pipeline to produce UD annotations for code-switched text. |
| Outcome: | The proposed pipeline outperforms existing parsers and baselines in syntactic analysis. |