Abstract
Recent improvements in KG-to-text generation are due to additional auxiliary pre-training tasks designed to give the fine-tune task a boost in performance. These tasks require extensive computational resources while only suggesting marginal improvements. Here, we demonstrate that by fusing graph-aware elements into existing pre-trained language models, we are able to outperform state-of-the-art models and close the gap imposed by additional pre-training tasks. We do so by proposing a mask structure to capture neighborhood information and a novel type encoder that adds a bias to the graph-attention weights depending on the connection type. Experiments on two KG-to-text benchmark datasets show our models are competitive while involving fewer parameters and no additional pre-training tasks. By formulating the problem as a framework, we can interchange the various proposed components and begin interpreting KG-to-text generative models based on the topological and type information found in a graph.- Anthology ID:
- 2022.coling-1.506
- 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:
- 5755–5769
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.506
- DOI:
- Cite (ACL):
- Anthony Colas, Mehrdad Alvandipour, and Daisy Zhe Wang. 2022. GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5755–5769, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation (Colas et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.coling-1.506.pdf
- Code
- acolas1/GAP_COLING2022
- Data
- EventNarrative, WebNLG