We study the influence of context on sentence acceptability. First we compare the acceptability ratings of sentences judged in isolation, with a relevant context, and with an irrelevant context. Our results show that context induces a cognitive load for humans, which compresses the distribution of ratings. Moreover, in relevant contexts we observe a discourse coherence effect which uniformly raises acceptability. Next, we test unidirectional and bidirectional language models in their ability to predict acceptability ratings. The bidirectional models show very promising results, with the best model achieving a new state-of-the-art for unsupervised acceptability prediction. The two sets of experiments provide insights into the cognitive aspects of sentence processing and central issues in the computational modelling of text and discourse.
翻译:我们研究上下文对判决可接受性的影响。首先,我们比较孤立、相关和不相关的判决的可接受性评级。我们的结果显示,背景给人类带来了认知负担,这压缩了评级的分布。此外,在相关情况下,我们观察了谈话一致性效应,这统一提高了可接受性。接下来,我们测试单向和双向语言模型,以它们预测可接受评级的能力。双向模型显示了非常有希望的结果,最佳模型为不受监督的可接受性预测提供了新的最新水平。两套实验为判决处理认知方面以及文本和谈话计算模型中的中心问题提供了深刻的见解。