  Chinese word segmentation and part-of-speech tagging are necessary tasks in
terms of computational linguistics and application of natural language
processing. Many re-searchers still debate the demand for Chinese word
segmentation and part-of-speech tagging in the deep learning era. Nevertheless,
resolving ambiguities and detecting unknown words are challenging problems in
this field. Previous studies on joint Chinese word segmentation and
part-of-speech tagging mainly follow the character-based tagging model focusing
on modeling n-gram features. Unlike previous works, we propose a neural model
named SpanSegTag for joint Chinese word segmentation and part-of-speech tagging
following the span labeling in which the probability of each n-gram being the
word and the part-of-speech tag is the main problem. We use the biaffine
operation over the left and right boundary representations of consecutive
characters to model the n-grams. Our experiments show that our BERT-based model
SpanSegTag achieved competitive performances on the CTB5, CTB6, and UD, or
significant improvements on CTB7 and CTB9 benchmark datasets compared with the
current state-of-the-art method using BERT or ZEN encoders.
