The Structured Weighted Violations MIRA
This work addresses sequence labeling problems in NLP, but it is incremental as it combines existing algorithms without introducing a fundamentally new approach.
The authors tackled structured prediction for sequence labeling tasks by hybridizing MIRA and SWVP, resulting in the SWVM algorithm that substantially outperforms existing methods like MIRA, structured perceptron, and SWVP in syntactic chunking and named entity recognition experiments.
We present the Structured Weighted Violation MIRA (SWVM), a new structured prediction algorithm that is based on an hybridization between MIRA (Crammer and Singer, 2003) and the structured weighted violations perceptron (SWVP) (Dror and Reichart, 2016). We demonstrate that the concepts developed in (Dror and Reichart, 2016) combined with a powerful structured prediction algorithm can improve performance on sequence labeling tasks. In experiments with syntactic chunking and named entity recognition (NER), the new algorithm substantially outperforms the original MIRA as well as the original structured perceptron and SWVP. Our code is available at https://github.com/dorringel/SWVM.