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تسجيل مستخدم جديدEnd-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems
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نشر من قبل
Cicero Nogueira Dos Santos
تاريخ النشر
2020
مجال البحث
الهندسة المعلوماتية
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English
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We propose an end-to-end approach for synthetic QA data generation. Our model comprises a single transformer-based encoder-decoder network that is trained end-to-end to generate both answers and questions. In a nutshell, we feed a passage to the encoder and ask the decoder to generate a question and an answer token-by-token. The likelihood produced in the generation process is used as a filtering score, which avoids the need for a separate filtering model. Our generator is trained by fine-tuning a pretrained LM using maximum likelihood estimation. The experimental results indicate significant improvements in the domain adaptation of QA models outperforming current state-of-the-art methods.
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