We cast sentence boundary detection and syntactic parsing as a joint problem, so an entire text document forms a training instance for transition-based dependency parsing. When trained with an early update or max-violation strategy for inexact search, we observe that only a tiny part of these very long training instances is ever exploited. We demonstrate this effect by extending the ArcStandard transition system with swap for the joint prediction task. When we use an alternative update strategy, our models are considerably better on both tasks and train in substantially less time compared to models trained with early update/max-violation. A comparison between a standard pipeline and our joint model furthermore empirically shows the usefulness of syntactic information on the task of sentence boundary detection.
['Anders Björkelund', 'Agnieszka Faleńska', 'Wolfgang Seeker', 'Jonas Kuhn']
How to Train Dependency Parsers with Inexact Search for Joint Sentence Boundary Detection and Parsing of Entire Documents