Incorporating stronger syntactic biases into neural language models (LMs) is
a long-standing goal, but research in this area often focuses on modeling
English text, where constituent treebanks are readily available. Extending
constituent tree-based LMs to the multilingual setting, where dependency
treebanks are more common, is possible via dependency-to-constituency
conversion methods. However, this raises the question of which tree formats are
best for learning the model, and for which languages. We investigate this
question by training recurrent neural network grammars (RNNGs) using various
conversion methods, and evaluating them empirically in a multilingual setting.
We examine the effect on LM performance across nine conversion methods and five
languages through seven types of syntactic tests. On average, the performance
of our best model represents a 19 \% increase in accuracy over the worst choice
across all languages. Our best model shows the advantage over
sequential/overparameterized LMs, suggesting the positive effect of syntax
injection in a multilingual setting. Our experiments highlight the importance
of choosing the right tree formalism, and provide insights into making an
informed decision.

通过依存句法树到常量句法树的转换，使用递归神经网络语法模型（RNNGs）进行训练，并在多语言环境下进行了实证评估，来探讨在学习模型时哪种树形式最佳，对哪些语言最适用，并通过七种类型的句法测试比较了 9 种转换方法和 5 种语言的语言模型表现，研究结果显示，最佳模型相对于最差模型在所有语言中的精确度提高了 19％，也证明了在多语言环境中注入语法的积极作用。