Papers with Automatic methods
Ruddit: Norms of Offensiveness for English Reddit Comments (2021.acl-long)
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| Challenge: | Existing methods to detect offensive language have been limited by categorical labels . however, there are several challenges in the detection of such content . |
| Approach: | They analyze Reddit comments with fine-grained, real-valued offensiveness scores . they evaluate the ability of widely-used neural models to predict offensiveness . |
| Outcome: | The proposed method produces highly reliable offensiveness scores and can predict scores on reddit comments. |
Fine-tuning with HED-IT: The impact of human post-editing for dialogical language models (2024.findings-acl)
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Daniela Occhipinti, Michele Marchi, Irene Mondella, Huiyuan Lai, Felice Dell’Orletta, Malvina Nissim, Marco Guerini
| Challenge: | a recent study has focused on the quality of data generated by automatic methods for fine-tuning Language Models in languages less resourced than English. |
| Approach: | They investigate whether human intervention improves the quality of machine-generated dialogues . they use a large-scale dataset to fine-tune three different sizes of an LM . |
| Outcome: | The results show that human intervention can improve the quality of training data . larger models are less sensitive to data quality, while smaller models are more sensitive . |