  When training a neural network, it will quickly memorise some source-target
mappings from your dataset but never learn some others. Yet, memorisation is
not easily expressed as a binary feature that is good or bad: individual
datapoints lie on a memorisation-generalisation continuum. What determines a
datapoint's position on that spectrum, and how does that spectrum influence
neural models' performance? We address these two questions for neural machine
translation (NMT) models. We use the counterfactual memorisation metric to (1)
build a resource that places 5M NMT datapoints on a memorisation-generalisation
map, (2) illustrate how the datapoints' surface-level characteristics and a
models' per-datum training signals are predictive of memorisation in NMT, (3)
and describe the influence that subsets of that map have on NMT systems'
performance.
