Semantic Scholar Open Access 2022 171 sitasi

SCROLLS: Standardized CompaRison Over Long Language Sequences

Uri Shaham Elad Segal Maor Ivgi Avia Efrat Ori Yoran +6 lainnya

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Abstrak

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.

Topik & Kata Kunci

Computer Science Mathematics

Penulis (11)

Uri Shaham

Elad Segal

Maor Ivgi

Avia Efrat

Ori Yoran

Adi Haviv

Ankit Gupta

Wenhan Xiong

Mor Geva

Jonathan Berant

Omer Levy

Format Sitasi

APA MLA BibTeX

Shaham, U., Segal, E., Ivgi, M., Efrat, A., Yoran, O., Haviv, A. et al. (2022). SCROLLS: Standardized CompaRison Over Long Language Sequences. https://doi.org/10.18653/v1/2022.emnlp-main.823

Akses Cepat

Lihat di Sumber doi.org/10.18653/v1/2022.emnlp-main.823

Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Total Sitasi: 171×
Sumber Database: Semantic Scholar
DOI: 10.18653/v1/2022.emnlp-main.823
Akses: Open Access ✓