Bibliographic Details
| Title: |
DG2: Data Augmentation Through Document Grounded Dialogue Generation |
| Authors: |
Wu, Qingyang, Feng, Song, Chen, Derek, Joshi, Sachindra, Lastras, Luis A., Yu, Zhou |
| Publication Year: |
2021 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computation and Language |
| More Details: |
Collecting data for training dialog systems can be extremely expensive due to the involvement of human participants and need for extensive annotation. Especially in document-grounded dialog systems, human experts need to carefully read the unstructured documents to answer the users' questions. As a result, existing document-grounded dialog datasets are relatively small-scale and obstruct the effective training of dialogue systems. In this paper, we propose an automatic data augmentation technique grounded on documents through a generative dialogue model. The dialogue model consists of a user bot and agent bot that can synthesize diverse dialogues given an input document, which are then used to train a downstream model. When supplementing the original dataset, our method achieves significant improvement over traditional data augmentation methods. We also achieve great performance in the low-resource setting. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2112.08342 |
| Accession Number: |
edsarx.2112.08342 |
| Database: |
arXiv |
