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While sentence anomalies have been applied periodically for testing in NLP,we have yet to establish a picture of the precise status of anomaly informationin representations from NLP models. In this paper we aim to fill two primarygaps, focusing on the domain of syntactic anomalies. First, we explorefine-grained differences in anomaly encoding by designing probing tasks thatvary the hierarchical level at which anomalies occur in a sentence. Second, wetest not only models' ability to detect a given anomaly, but also thegenerality of the detected anomaly signal, by examining transfer betweendistinct anomaly types. Results suggest that all models encode some informationsupporting anomaly detection, but detection performance varies betweenanomalies, and only representations from more recent transformer models showsigns of generalized knowledge of anomalies. Follow-up analyses support thenotion that these models pick up on a legitimate, general notion of sentenceoddity, while coarser-grained word position information is likely also acontributor to the observed anomaly detection.

Variation and generality in encoding of syntactic anomaly information in sentence embeddings