  Many efforts have been devoted to extracting constituency trees from
pre-trained language models, often proceeding in two stages: feature definition
and parsing. However, this kind of methods may suffer from the branching bias
issue, which will inflate the performances on languages with the same branch it
biases to. In this work, we propose quantitatively measuring the branching bias
by comparing the performance gap on a language and its reversed language, which
is agnostic to both language models and extracting methods. Furthermore, we
analyze the impacts of three factors on the branching bias, namely parsing
algorithms, feature definitions, and language models. Experiments show that
several existing works exhibit branching biases, and some implementations of
these three factors can introduce the branching bias.
