DG2: Data Augmentation Through Document Grounded Dialogue Generation

• URL: http://arxiv.org/abs/2112.08342v1
• Date: Wed, 15 Dec 2021 18:50:14 GMT
• Title: DG2: Data Augmentation Through Document Grounded Dialogue Generation
• Authors: Qingyang Wu, Song Feng, Derek Chen, Sachindra Joshi, Luis A. Lastras, Zhou Yu
• Abstract summary: We propose an automatic data augmentation technique grounded on documents through a generative dialogue model. When supplementing the original dataset, our method achieves significant improvement over traditional data augmentation methods.
• Score: 41.81030088619399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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