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.

研究上下文对句子可接受性的影响，结论是上下文会使人们承受认知负担，从而压缩了评分分布，而在相关上下文中观察到的话语连贯性效应可以一致地提高接受度；此外，测试了单向和双向语言模型在预测可接受性评级方面的能力，表明双向模型取得了极具前景的结果，最佳模型实现了无监督接受性预测的最新水平。

无色的绿色观念能以多大的愤怒睡觉？上下文中的句子可接受性

How Furiously Can Colourless Green Ideas Sleep? Sentence Acceptability  in Context

RNN language models have achieved state-of-the-art results on various tasks,
but what exactly they are representing about syntax is as yet unclear. Here we
investigate whether RNN language models learn humanlike word order preferences
in syntactic alternations. We collect language model surprisal scores for
controlled sentence stimuli exhibiting major syntactic alternations in English:
heavy NP shift, particle shift, the dative alternation, and the genitive
alternation. We show that RNN language models reproduce human preferences in
these alternations based on NP length, animacy, and definiteness. We collect
human acceptability ratings for our stimuli, in the first acceptability
judgment experiment directly manipulating the predictors of syntactic
alternations. We show that the RNNs' performance is similar to the human
acceptability ratings and is not matched by an n-gram baseline model. Our
results show that RNNs learn the abstract features of weight, animacy, and
definiteness which underlie soft constraints on syntactic alternations.

通过构造符合主要句法变异的受控句子刺激，研究发现 RNN 语言模型能够复现人类在句法变异中基于名词短长、生命力和确定性等因素的偏好，并且能够与人类评估结果相匹配，考虑到 N-gram 基准模型的情况，这说明了 RNNs 学习了在句法变异中概括名词重量、生命力和确定性等抽象特征。