BERTGen: Multi-task Generation through BERT

Faidon Mitzalis, Ozan Caglayan, Pranava Madhyastha, Lucia Specia


Abstract
We present BERTGen, a novel, generative, decoder-only model which extends BERT by fusing multimodal and multilingual pre-trained models VL-BERT and M-BERT, respectively. BERTGen is auto-regressively trained for language generation tasks, namely image captioning, machine translation and multimodal machine translation, under a multi-task setting. With a comprehensive set of evaluations, we show that BERTGen outperforms many strong baselines across the tasks explored. We also show BERTGen’s ability for zero-shot language generation, where it exhibits competitive performance to supervised counterparts. Finally, we conduct ablation studies which demonstrate that BERTGen substantially benefits from multi-tasking and effectively transfers relevant inductive biases from the pre-trained models.
Anthology ID:
2021.acl-long.503
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6440–6455
Language:
URL:
https://aclanthology.org/2021.acl-long.503
DOI:
10.18653/v1/2021.acl-long.503
Bibkey:
Cite (ACL):
Faidon Mitzalis, Ozan Caglayan, Pranava Madhyastha, and Lucia Specia. 2021. BERTGen: Multi-task Generation through BERT. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 6440–6455, Online. Association for Computational Linguistics.
Cite (Informal):
BERTGen: Multi-task Generation through BERT (Mitzalis et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2021.acl-long.503.pdf
Video:
 https://preview.aclanthology.org/ingest-acl-2023-videos/2021.acl-long.503.mp4
Code
 ImperialNLP/BertGen