Papers with low-resource language setting
Multi3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue (2023.findings-acl)
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| Challenge: | Task-oriented dialogue systems are typically constructed for a single domain or language and do not generalise well beyond this. |
| Approach: | They constructed a multilingual, multi-intent, multi domain dataset to support work on Natural Language Understanding (NLU) in ToD across multiple languages and domains simultaneously. |
| Outcome: | The proposed dataset extends the English-only dataset to include manual translations into a range of high, medium, and low resource languages in two domains (banking and hotels). |
MarathiEmoExplain: A Dataset for Sentiment, Emotion, and Explanation in Low-Resource Marathi (2025.findings-emnlp)
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| Challenge: | Marathi is the third most widely spoken language in India with over 83 million native speakers . available Marath datasets are limited to coarse sentiment labels and lack fine-grained emotional categorization or interpretability through explanations. |
| Approach: | They propose to annotate Marathi sentences labeled with sentiment, emotion and a corresponding natural language justification. |
| Outcome: | The proposed dataset provides a benchmark for future research in multilingual and explainable NLP. |
MassiveSumm: a very large-scale, very multilingual, news summarisation dataset (2021.emnlp-main)
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| Challenge: | Current research in automatic summarisation is expensive to create, posing a challenge for any language. |
| Approach: | They propose to use a large-scale multilingual summarisation dataset with articles in 92 languages and more than 35 writing scripts to generate a multilingual dataset. |
| Outcome: | The proposed method is the largest, most inclusive, existing dataset and one of the largest and most inclusive datasets for any NLP task. |