DiscoSense: Commonsense Reasoning with Discourse Connectives
Abstract
DiscoSense, a benchmark for evaluating commonsense reasoning through discourse connectives, uses Conditional Adversarial Filtering to generate challenging distractors, revealing limitations in state-of-the-art pre-trained language models.
We present DiscoSense, a benchmark for commonsense reasoning via understanding a wide variety of discourse connectives. We generate compelling distractors in DiscoSense using Conditional Adversarial Filtering, an extension of Adversarial Filtering that employs conditional generation. We show that state-of-the-art pre-trained language models struggle to perform well on DiscoSense, which makes this dataset ideal for evaluating next-generation commonsense reasoning systems.
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