  Keystroke dynamics have been extensively used in psycholinguistic and writing
research to gain insights into cognitive processing. But do keystroke logs
contain actual signal that can be used to learn better natural language
processing models?
  We postulate that keystroke dynamics contain information about syntactic
structure that can inform shallow syntactic parsing. To test this hypothesis,
we explore labels derived from keystroke logs as auxiliary task in a multi-task
bidirectional Long Short-Term Memory (bi-LSTM). Our results show promising
results on two shallow syntactic parsing tasks, chunking and CCG supertagging.
Our model is simple, has the advantage that data can come from distinct
sources, and produces models that are significantly better than models trained
on the text annotations alone.
