One of the key features of natural languages is that they exhibit long-distance filler-gap dependencies (FGDs): In the sentence "What do you think the pilot sent __?" the wh-filler "what" is interpreted as the object of the verb "sent" across multiple words. The ability to establish FGDs is thought to require hierarchical syntactic structure. However, recent research suggests that recurrent neural networks (RNNs) without specific hierarchical bias can learn complex generalizations about wh-questions in English from raw text data (Wilcox et al. 2018; 2019). Across two experiments, we probe the generality of this result by testing whether a long short-term memory (LSTM) RNN model can learn basic generalizations about FGDs in Norwegian. Testing Norwegian allows us to assess whether previous results were due to distributional statistics of the English input or whether models can extract similar generalizations in languages with different syntactic distributions. We also test the model's performance on two different FGDs: wh-questions and relative clauses, allowing us to determine if the model learns abstract generalizations about FGDs that extend beyond a single construction type. Results from Experiment 1 suggest that the model expects fillers to be paired with gaps and that this expectation generalizes across different syntactic positions. Results from Experiment 2 suggest that the model's expectations are largely unaffected by the increased linear distance between the filler and the gap. Our findings provide support for the conclusion that LSTM RNN's ability to learn basic generalizations about FGDs is robust across dependency type and language.

Recent research suggests that Recurrent Neural Networks (RNNs) can capture abstract generalizations about filler-gap dependencies (FGDs) in English and so-called island constraints on their distribution (Wilcox et al., 2018; 2021). These results have been interpreted as evidence that it is possible, in principle, to induce complex syntactic knowledge from the input without domain-specific learning biases. However, the English results alone do not establish that island constraints were induced from distributional properties of the training data instead of simply reflecting architectural limitations independent of the input to the models. We address this concern by investigating whether such models can learn the distribution of acceptable FGDs in Norwegian, a language that is sensitive to fewer islands than English (Christensen, 1982). Results from five experiments show that Long Short-Term Memory (LSTM) RNNs can (i) learn that Norwegian FGD formation is unbounded, (ii) recover the island status of temporal adjunct and subject islands, and (iii) learn that Norwegian, unlike English, permits FGDs into two types of embedded questions. The fact that LSTM RNNs can learn cross-linguistic differences in island facts therefore strengthens the claim that RNN language models can induce the constraints from patterns in the input.