  In the pre deep learning era, part-of-speech tags have been considered as
indispensable ingredients for feature engineering in dependency parsing. But
quite a few works focus on joint tagging and parsing models to avoid error
propagation. In contrast, recent studies suggest that POS tagging becomes much
less important or even useless for neural parsing, especially when using
character-based word representations. Yet there are not enough investigations
focusing on this issue, both empirically and linguistically. To answer this, we
design and compare three typical multi-task learning framework, i.e.,
Share-Loose, Share-Tight, and Stack, for joint tagging and parsing based on the
state-of-the-art biaffine parser. Considering that it is much cheaper to
annotate POS tags than parse trees, we also investigate the utilization of
large-scale heterogeneous POS tag data. We conduct experiments on both English
and Chinese datasets, and the results clearly show that POS tagging (both
homogeneous and heterogeneous) can still significantly improve parsing
performance when using the Stack joint framework. We conduct detailed analysis
and gain more insights from the linguistic aspect.
