Characterizing how 'distributional' NLP corpora distance metrics are
Samuel Ackerman George Kour Eitan Farchi
A corpus of vector-embedded text documents has some empirical distribution.
Given two corpora, we want to calculate a single metric of distance (e.g.,
Mauve, Frechet Inception) between them. We describe an abstract quality, called
`distributionality', of such metrics. A non-distributional metric tends to use
very local measurements, or uses global measurements in a way that does not
fully reflect the distributions' true distance. For example, if individual
pairwise nearest-neighbor distances are low, it may judge the two corpora to
have low distance, even if their two distributions are in fact far from each
other. A more distributional metric will, in contrast, better capture the
distributions' overall distance. We quantify this quality by constructing a
Known-Similarity Corpora set from two paraphrase corpora and calculating the
distance between paired corpora from it. The distances' trend shape as set
element separation increases should quantify the distributionality of the
metric. We propose that Average Hausdorff Distance and energy distance between
corpora are representative examples of non-distributional and distributional
distance metrics, to which other metrics can be compared, to evaluate how
distributional they are.