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Because meaning can often be inferred from lexical semantics alone, wordorder is often a redundant cue in natural language. For example, the wordschopped, chef, and onion are more likely used to convey "The chef chopped theonion," not "The onion chopped the chef." Recent work has shown large languagemodels to be surprisingly word order invariant, but crucially has largelyconsidered natural prototypical inputs, where compositional meaning mostlymatches lexical expectations. To overcome this confound, we probe grammaticalrole representation in English BERT and GPT-2, on instances where lexicalexpectations are not sufficient, and word order knowledge is necessary forcorrect classification. Such non-prototypical instances are naturally occurringEnglish sentences with inanimate subjects or animate objects, or sentenceswhere we systematically swap the arguments to make sentences like "The onionchopped the chef". We find that, while early layer embeddings are largelylexical, word order is in fact crucial in defining the later-layerrepresentations of words in semantically non-prototypical positions. Ourexperiments isolate the effect of word order on the contextualization process,and highlight how models use context in the uncommon, but critical, instanceswhere it matters.

When classifying grammatical role, BERT doesn't care about word order... except when it matters