Adapting Pretrained Text-to-Text Models for Long Text Sequences Article Swipe
Wenhan Xiong
,
Anchit Gupta
,
Shubham Toshniwal
,
Yashar Mehdad
,
Wen-tau Yih
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2209.10052
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2209.10052
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 length. 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. Our code has been released at https://github.com/facebookresearch/bart_ls.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2209.10052
- https://arxiv.org/pdf/2209.10052
- OA Status
- green
- Cited By
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4297899311
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4297899311Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2209.10052Digital Object Identifier
- Title
-
Adapting Pretrained Text-to-Text Models for Long Text SequencesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-21Full publication date if available
- Authors
-
Wenhan Xiong, Anchit Gupta, Shubham Toshniwal, Yashar Mehdad, Wen-tau YihList of authors in order
- Landing page
-
https://arxiv.org/abs/2209.10052Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2209.10052Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2209.10052Direct OA link when available
- Concepts
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Computer science, Automatic summarization, Natural language processing, Pipeline (software), Artificial intelligence, Transformer, Context (archaeology), Language model, Pooling, Speech recognition, Information retrieval, Programming language, Physics, Paleontology, Quantum mechanics, Voltage, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
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2023: 4, 2022: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.pretraining | 24, 32, 77 |
| abstract_inverted_index.concatenated | 84 |
| abstract_inverted_index.long-context | 41, 115 |
| abstract_inverted_index.optimization | 29 |
| abstract_inverted_index.text-to-text | 10 |
| abstract_inverted_index.transformers | 54 |
| abstract_inverted_index.Specifically, | 47 |
| abstract_inverted_index.architecture, | 28 |
| abstract_inverted_index.comprehensive | 17 |
| abstract_inverted_index.long-sequence | 13 |
| abstract_inverted_index.outperforming | 139 |
| abstract_inverted_index.short-context | 45 |
| abstract_inverted_index.summarization | 136 |
| abstract_inverted_index.short-documents | 85 |
| abstract_inverted_index.pooling-augmented | 56 |
| abstract_inverted_index.https://github.com/facebookresearch/bart_ls. | 152 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |