TeClass: A Human-Annotated Relevance-based Headline Classification and Generation Dataset for Telugu (2024.lrec-main)
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| Challenge: | Relevance-based headline classification is under-explored in low-resource languages like Telugu due to a lack of annotated data. |
| Approach: | They propose that relevance-based headline classification can greatly aid the task of generating relevant headlines. |
| Outcome: | The proposed model can generate relevant headlines with 78,534 annotations in Telugu . the model shows a 5 point increment in the ROUGE-L scores . |
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| Challenge: | Existing datasets for low-resource languages lack labelled data . public datasets only cover low-level syntactic tasks . |
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| Challenge: | XL-HeadTags is a dataset that includes 20 languages across 6 diverse language families. |
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