Error propagation is a common problem in NLP. Reinforcement learning explores erroneous states during training and can therefore be more robust when mistakes are made early in a process. In this paper, we apply reinforcement learning to greedy dependency parsing which is known to suffer from error propagation. Reinforcement learning improves accuracy of both labeled and unlabeled dependencies of the Stanford Neural Dependency Parser, a high performance greedy parser, while maintaining its efficiency. We investigate the portion of errors which are the result of error propagation and confirm that reinforcement learning reduces the occurrence of error propagation.
|Title of host publication||Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics|
|Place of Publication||Valencia, Spain|
|Number of pages||10|
|Publication status||Published - Apr 2017|