Parsing linearizations appreciate PoS tags - but some are fussy about errors
This work addresses the role of PoS tags in modern parsing for NLP researchers, providing insights into their conditional utility in sequence labeling methods.
The study investigated the impact of PoS tag accuracy on sequence labeling parsers, finding that PoS tags are generally more useful for this paradigm than for others, but their effectiveness depends heavily on encoding, with PoS-based head-selection encoding performing best only under high tagging accuracy and resource availability.
PoS tags, once taken for granted as a useful resource for syntactic parsing, have become more situational with the popularization of deep learning. Recent work on the impact of PoS tags on graph- and transition-based parsers suggests that they are only useful when tagging accuracy is prohibitively high, or in low-resource scenarios. However, such an analysis is lacking for the emerging sequence labeling parsing paradigm, where it is especially relevant as some models explicitly use PoS tags for encoding and decoding. We undertake a study and uncover some trends. Among them, PoS tags are generally more useful for sequence labeling parsers than for other paradigms, but the impact of their accuracy is highly encoding-dependent, with the PoS-based head-selection encoding being best only when both tagging accuracy and resource availability are high.