training data as unambiguous RC attachment. Within that 10%, we varied the proportion of HIGH and LOW attachment in 10% increments (i.e. 0% HIGH - 100% LOW, 10% HIGH - 90% LOW, etc). Once again, we trained 5 models on each training configuration and tested those models on 300 test sentences, measuring the surprisal at the RC verb. When the training data had 50-100% HIGH attachment, the models preferred HIGH attachment in all the test sentences. Conversely, when the training data had 0-40% HIGH attachment, the models preferred LOW attachment in all test sentences.

Taken together, the results from our synthetic language experiments suggest that HIGH attachment is indeed learnable by RNN LMs. In fact, an equal proportion of HIGH and LOW attachment in the training data is all that is needed for these models to acquire a general preference for HIGH attachment (contra to the recency bias reported in the literature).

English Experiments {#fernandezExps}
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We turn now to model attachment preferences in English. We trained the models using English Wikipedia. We tested the attachment preferences of the RNN LMs using the original stimuli from @fernadez2003bilingual, and using a larger set of stimuli to have a better sense of 