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

arXiv: https://arxiv.org/abs/2004.00881 · HTML: https://mlsanity.com/paper/2004.00881/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2020.

## Built on by (most cited first, 26 of 26)

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- [RuCoLA: Russian Corpus of Linguistic Acceptability](https://mlsanity.com/paper/2210.12814/) (2022)
- [Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus](https://mlsanity.com/paper/2109.12053/) (2021)
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- [Evaluating the Efficacy of Summarization Evaluation across Languages](https://mlsanity.com/paper/2106.01478/) (2021)
- [How to Make the Most of LLMs' Grammatical Knowledge for Acceptability Judgments](https://mlsanity.com/paper/2408.09639/) (2024)
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- [Can BERT eat RuCoLA? Topological Data Analysis to Explain](https://mlsanity.com/paper/2304.01680/) (2023)
- [Multilingual Nonce Dependency Treebanks: Understanding how Language Models represent and process syntactic structure](https://mlsanity.com/paper/2311.07497/) (2023)
- [Language Model Evaluation in Open-ended Text Generation](https://mlsanity.com/paper/2108.03578/) (2021)
- [Predicting Sentence Acceptability Judgments in Multimodal Contexts](https://mlsanity.com/paper/2602.20918/) (2026)
- [NaturalAdversaries: Can Naturalistic Adversaries Be as Effective as Artificial Adversaries?](https://mlsanity.com/paper/2211.04364/) (2022)
- [Unsupervised Paraphrasing of Multiword Expressions](https://mlsanity.com/paper/2306.01443/) (2023)
- [Do Large Language Models know who did what to whom?](https://mlsanity.com/paper/2504.16884/) (2025)
- [Finding Sense in Nonsense with Generated Contexts: Perspectives from Humans and Language Models](https://mlsanity.com/paper/2602.11699/) (2026)
- [LLM and Human Modes of Representation](https://mlsanity.com/paper/2606.21616/) (2026)

## Abstract (arXiv)

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.