Papers with filtering model
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems (2020.emnlp-main)
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Siamak Shakeri, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Feng Nan, Zhiguo Wang, Ramesh Nallapati, Bing Xiang
| Challenge: | Existing approaches for synthetic QA data generation have limited or no success in improving the downstream Reading Comprehension task. |
| Approach: | They propose an end-to-end approach for synthetic QA data generation using a transformer-based encoder-decoder network that is trained end- to-end to generate both answers and questions. |
| Outcome: | The proposed model outperforms current state-of-the-art methods in the domain adaptation of QA models. |
Multilingual Data Filtering using Synthetic Data from Large Language Models (2025.findings-emnlp)
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| Challenge: | Recent studies have shown that effective filters can be created by utilising Large Language Models to synthetically label data, which is then used to train smaller neural models for filtering purposes. |
| Approach: | They extend this approach to languages beyond English to train neural models for filtering purposes. |
| Outcome: | The proposed approach is effective at filtering parallel text for translation quality and filtering for domain specificity. |