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Models pre-trained with a language modeling objective possess ample worldknowledge and language skills, but are known to struggle in tasks that requirereasoning. In this work, we propose to leverage semi-structured tables, andautomatically generate at scale question-paragraph pairs, where answering thequestion requires reasoning over multiple facts in the paragraph. We add apre-training step over this synthetic data, which includes examples thatrequire 16 different reasoning skills such as number comparison, conjunction,and fact composition. To improve data efficiency, we propose samplingstrategies that focus training on reasoning skills the model is currentlylacking. We evaluate our approach on three reading comprehension datasets thatare focused on reasoning, and show that our model, PReasM, substantiallyoutperforms T5, a popular pre-trained encoder-decoder model. Moreover, samplingexamples based on current model errors leads to faster training and higheroverall performance.

Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills