Papers with curriculum learning methods
Pisets: A Robust Speech Recognition System for Lectures and Interviews (2025.naacl-industry)
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Ivan Bondarenko, Daniil Grebenkin, Oleg Sedukhin, Mikhail Klementev, Derunets Roman, Lyudmila Budneva
| Challenge: | Sustainable speech recognition systems are essential for scientists, journalists, and anyone processing audio recordings of interviews and meetings. |
| Approach: | They propose a speech-to-text system "Pisets" which is based on a three-component architecture aimed at improving speech recognition accuracy while minimizing errors and hallucinations associated with the Whisper model. |
| Outcome: | The proposed system ensures robust transcribing of long audio data across various acoustic conditions compared to WhisperX and the usual Whisper model. |
Applying Natural Annotation and Curriculum Learning to Named Entity Recognition for Under-Resourced Languages (2022.coling-1)
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| Challenge: | Existing approaches to build NLP models for low-resourced languages rely on machine translation or cross-lingual transfer. |
| Approach: | They propose to use natural annotations to build synthetic training sets from resources not originally designed for the target downstream task. |
| Outcome: | The proposed model achieves the F1 score of 0.78 for Belarusian starting from zero resources compared to the baseline of 0.63 for English . the proposed model can be fine-tuned to reflect linguistic properties, such as the grammatical case and gender, for the Slavic languages. |
Cross-Modal Similarity-Based Curriculum Learning for Image Captioning (2022.emnlp-main)
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| Challenge: | Existing image captioning approaches treat image-caption pairs indistinctly without considering the differences in their learning difficulties. |
| Approach: | They propose a pretrained vision–language model that measures cross-modal similarity and a model that uses cross-module similarity to measure the difficulty of captioning. |
| Outcome: | The proposed model achieves superior performance and competitive convergence speed to baselines without incurring additional training costs. |