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.

通过使用反事实记忆度量标准，建立了一个资源，将 500 万条神经机器翻译数据点放置在一个记忆 - 泛化地图上，并展示了数据点的表面特征和模型的训练信号如何预测神经机器翻译中的记忆，以及该地图的子集对神经机器翻译系统性能的影响。