Abstract
Recent advances in commonsense reasoning have been fueled by the availability of large-scale human annotated datasets. Manual annotation of such datasets, many of which are based on existing knowledge bases, is expensive and not scalable. Moreover, it is challenging to build augmentation data for commonsense reasoning because the synthetic questions need to adhere to real-world scenarios. Hence, we present GraDA, a graph-generative data augmentation framework to synthesize factual data samples from knowledge graphs for commonsense reasoning datasets. First, we train a graph-to-text model for conditional generation of questions from graph entities and relations. Then, we train a generator with GAN loss to generate distractors for synthetic questions. Our approach improves performance for SocialIQA, CODAH, HellaSwag and CommonsenseQA, and works well for generative tasks like ProtoQA. We show improvement in robustness to semantic adversaries after training with GraDA and provide human evaluation of the quality of synthetic datasets in terms of factuality and answerability. Our work provides evidence and encourages future research into graph-based generative data augmentation.- Anthology ID:
- 2022.coling-1.397
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 4499–4516
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.397
- DOI:
- Cite (ACL):
- Adyasha Maharana and Mohit Bansal. 2022. GraDA: Graph Generative Data Augmentation for Commonsense Reasoning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4499–4516, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- GraDA: Graph Generative Data Augmentation for Commonsense Reasoning (Maharana & Bansal, COLING 2022)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2022.coling-1.397.pdf
- Code
- adymaharana/grada
- Data
- CODAH, CommonsenseQA, ConceptNet, HellaSwag, ProtoQA