As it has been unveiled that pre-trained language models (PLMs) are to some
extent capable of recognizing syntactic concepts in natural language, much
effort has been made to develop a method for extracting complete (binary)
parses from PLMs without training separate parsers. We improve upon this
paradigm by proposing a novel chart-based method and an effective top-K
ensemble technique. Moreover, we demonstrate that we can broaden the scope of
application of the approach into multilingual settings. Specifically, we show
that by applying our method on multilingual PLMs, it becomes possible to induce
non-trivial parses for sentences from nine languages in an integrated and
language-agnostic manner, attaining performance superior or comparable to that
of unsupervised PCFGs. We also verify that our approach is robust to
cross-lingual transfer. Finally, we provide analyses on the inner workings of
our method. For instance, we discover universal attention heads which are
consistently sensitive to syntactic information irrespective of the input
language.

本研究开发了一种基于图表的方法和有效的 top-K 集成技术，通过应用这种方法到多语言的预训练语言模型中，可以在集成和语言无关的方式下诱导出九种语言的句子的非平凡解析，同时发现了普遍的注意力头，这些头与句法信息的感知是一致的，验证了该方法的鲁棒性。