Ori Yoran


Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills
Ori Yoran | Alon Talmor | Jonathan Berant
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Models pre-trained with a language modeling objective possess ample world knowledge and language skills, but are known to struggle in tasks that require reasoning. In this work, we propose to leverage semi-structured tables, and automatically generate at scale question-paragraph pairs, where answering the question requires reasoning over multiple facts in the paragraph. We add a pre-training step over this synthetic data, which includes examples that require 16 different reasoning skills such as number comparison, conjunction, and fact composition. To improve data efficiency, we sample examples from reasoning skills where the model currently errs. We evaluate our approach on three reasoning-focused reading comprehension datasets, and show that our model, PReasM, substantially outperforms T5, a popular pre-trained encoder-decoder model. Moreover, sampling examples based on model errors leads to faster training and higher performance.

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
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

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.