End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems

Siamak Shakeri, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Feng Nan, Zhiguo Wang, Ramesh Nallapati, Bing Xiang


Abstract
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.
Anthology ID:
2020.emnlp-main.439
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5445–5460
Language:
URL:
https://aclanthology.org/2020.emnlp-main.439
DOI:
10.18653/v1/2020.emnlp-main.439
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.439.pdf
Video:
 https://slideslive.com/38939002
Data
BioASQDuoRCNatural Questions