Paragraph-based Transformer Pre-training for Multi-Sentence Inference
Luca Di Liello, Siddhant Garg, Luca Soldaini, Alessandro Moschitti
Abstract
Inference tasks such as answer sentence selection (AS2) or fact verification are typically solved by fine-tuning transformer-based models as individual sentence-pair classifiers. Recent studies show that these tasks benefit from modeling dependencies across multiple candidate sentences jointly. In this paper, we first show that popular pre-trained transformers perform poorly when used for fine-tuning on multi-candidate inference tasks. We then propose a new pre-training objective that models the paragraph-level semantics across multiple input sentences. Our evaluation on three AS2 and one fact verification datasets demonstrates the superiority of our pre-training technique over the traditional ones for transformers used as joint models for multi-candidate inference tasks, as well as when used as cross-encoders for sentence-pair formulations of these tasks.- Anthology ID:
- 2022.naacl-main.181
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2521–2531
- Language:
- URL:
- https://aclanthology.org/2022.naacl-main.181
- DOI:
- 10.18653/v1/2022.naacl-main.181
- Cite (ACL):
- Luca Di Liello, Siddhant Garg, Luca Soldaini, and Alessandro Moschitti. 2022. Paragraph-based Transformer Pre-training for Multi-Sentence Inference. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2521–2531, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Paragraph-based Transformer Pre-training for Multi-Sentence Inference (Di Liello et al., NAACL 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.naacl-main.181.pdf
- Code
- amazon-research/wqa-multi-sentence-inference
- Data
- ASNQ, FEVER, TrecQA, WikiQA