SCROLLS: Standardized CompaRison Over Long Language Sequences
Uri Shaham, Elad Segal, Maor Ivgi, Avia Efrat, Ori Yoran, Adi Haviv, Ankit Gupta, Wenhan Xiong, Mor Geva, Jonathan Berant, Omer Levy
Abstract
NLP benchmarks have largely focused on short texts, such as sentences and paragraphs, even though long texts comprise a considerable amount of natural language in the wild. We introduce SCROLLS, a suite of tasks that require reasoning over long texts. We examine existing long-text datasets, and handpick ones where the text is naturally long, while prioritizing tasks that involve synthesizing information across the input. SCROLLS contains summarization, question answering, and natural language inference tasks, covering multiple domains, including literature, science, business, and entertainment. Initial baselines, including Longformer Encoder-Decoder, indicate that there is ample room for improvement on SCROLLS. We make all datasets available in a unified text-to-text format and host a live leaderboard to facilitate research on model architecture and pretraining methods.- Anthology ID:
- 2022.emnlp-main.823
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 12007–12021
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.823
- DOI:
- 10.18653/v1/2022.emnlp-main.823
- Cite (ACL):
- Uri Shaham, Elad Segal, Maor Ivgi, Avia Efrat, Ori Yoran, Adi Haviv, Ankit Gupta, Wenhan Xiong, Mor Geva, Jonathan Berant, and Omer Levy. 2022. SCROLLS: Standardized CompaRison Over Long Language Sequences. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 12007–12021, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- SCROLLS: Standardized CompaRison Over Long Language Sequences (Shaham et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2022.emnlp-main.823.pdf