  Prior work has shown that structural supervision helps English language
models learn generalizations about syntactic phenomena such as subject-verb
agreement. However, it remains unclear if such an inductive bias would also
improve language models' ability to learn grammatical dependencies in
typologically different languages. Here we investigate this question in
Mandarin Chinese, which has a logographic, largely syllable-based writing
system; different word order; and sparser morphology than English. We train
LSTMs, Recurrent Neural Network Grammars, Transformer language models, and
Transformer-parameterized generative parsing models on two Mandarin Chinese
datasets of different sizes. We evaluate the models' ability to learn different
aspects of Mandarin grammar that assess syntactic and semantic relationships.
We find suggestive evidence that structural supervision helps with representing
syntactic state across intervening content and improves performance in low-data
settings, suggesting that the benefits of hierarchical inductive biases in
acquiring dependency relationships may extend beyond English.
