Adapting Pretrained Text-to-Text Models for Long Text Sequences

Wenhan Xiong, Anchit Gupta, Shubham Toshniwal, Yashar Mehdad, Scott Yih


Abstract
We present an empirical study of adapting an existing pretrained text-to-text model for long-sequence inputs. Through a comprehensive study along three axes of the pretraining pipeline – model architecture, optimization objective, and pretraining corpus, we propose an effective recipe to build long-context models from existing short-context models. Specifically, we replace the full attention in transformers with pooling-augmented blockwise attention, and pretrain the model with a masked-span prediction task with spans of varying lengths. In terms of the pretraining corpus, we find that using randomly concatenated short-documents from a large open-domain corpus results in better performance than using existing long document corpora, which are typically limited in their domain coverage. With these findings, we build a long-context model that achieves competitive performance on long-text QA tasks and establishes the new state of the art on five long-text summarization datasets, often outperforming previous methods with larger model sizes.
Anthology ID:
2023.findings-emnlp.370
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5566–5578
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.370
DOI:
10.18653/v1/2023.findings-emnlp.370
Bibkey:
Cite (ACL):
Wenhan Xiong, Anchit Gupta, Shubham Toshniwal, Yashar Mehdad, and Scott Yih. 2023. Adapting Pretrained Text-to-Text Models for Long Text Sequences. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 5566–5578, Singapore. Association for Computational Linguistics.
Cite (Informal):
Adapting Pretrained Text-to-Text Models for Long Text Sequences (Xiong et al., Findings 2023)
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PDF:
https://aclanthology.org/2023.findings-emnlp.370.pdf