  Since their inception, encoder-decoder models have successfully been applied
to a wide array of problems in computational linguistics. The most recent
successes are predominantly due to the use of different variations of attention
mechanisms, but their cognitive plausibility is questionable. In particular,
because past representations can be revisited at any point in time,
attention-centric methods seem to lack an incentive to build up incrementally
more informative representations of incoming sentences. This way of processing
stands in stark contrast with the way in which humans are believed to process
language: continuously and rapidly integrating new information as it is
encountered. In this work, we propose three novel metrics to assess the
behavior of RNNs with and without an attention mechanism and identify key
differences in the way the different model types process sentences.
