Artificially Evolved Chunks for Morphosyntactic Analysis
This work addresses language processing challenges for NLP researchers, but it is incremental as it builds on existing chunking and multi-task methods.
The paper tackles the problem of morphosyntactic analysis by introducing an evolutionary technique to extract chunks from dependency treebanks, showing that these chunks improve performance on tasks like POS tagging and dependency parsing across English and non-English treebanks.
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.