We introduce a language-agnostic evolutionary technique for automatically extracting chunks from dependency treebanks. We evaluate these chunks on a number of morphosyntactic tasks, namely POS tagging, morphological feature tagging, and dependency parsing. We test the utility of these chunks in a host of different ways. We first learn chunking as one task in a shared multi-task framework together with POS and morphological feature tagging. The predictions from this network are then used as input to augment sequence-labelling dependency parsing. Finally, we investigate the impact chunks have on dependency parsing in a multi-task framework. Our results from these analyses show that these chunks improve performance at different levels of syntactic abstraction on English UD treebanks and a small, diverse subset of non-English UD treebanks.
@article{arxiv.1908.03480,
title = {Artificially Evolved Chunks for Morphosyntactic Analysis},
author = {Mark Anderson and David Vilares and Carlos Gómez-Rodríguez},
journal= {arXiv preprint arXiv:1908.03480},
year = {2019}
}
Comments
To be published in proceedings of the 18th International Workshop on Treebanks and Linguistic Theories