On Parsing as Tagging
Afra Amini, Ryan Cotterell · 2022
There have been many proposals to reduce constituency parsing to tagging in the literature.To better understand what these approaches have in common, we cast several existing proposals into a unifying pipeline consisting of three steps: linearization, learning, and decoding.In particular, we show how to reduce tetratagging, a state-of-the-art constituency tagger, to shift-reduce parsing by performing a right-corner transformation on the grammar and making a specific independence assumption.Furthermore, we empirically evaluate our taxonomy of tagging pipelines with different choices of linearizers, learners, and decoders.Based on the results in English and a set of 8 typologically diverse languages, we conclude that the linearization of the derivation tree and its alignment with the input sequence is the most critical factor in achieving accurate taggers.https://github.com/rycolab/ parsing-as-tagging