… for clustering, and describe a taxonomy of evaluation criteria for unsupervised machine
learning. We also survey many of the evaluation metrics that have been proposed in the literature…

… We conclude with an evaluation of the relative utility of a large array of different metrics and
… is a report on a language learning project, our approach to evaluating metrics is in no way …

… a metric to quantify the distance between categorical data for unsupervised learning well. In
this … The evaluations of clustering outcomes obtained by the k-modes algorithm with different …

… We investigate evaluation metrics for dialogue … metrics from machine translation to
compare a model’s generated response to a single target response. We show that these metrics …

… 2018), but all either lack certain categories of unsupervised metric or lack human validation
of them, which we contribute. Moreover, the textual transfer community lacks discussion of …

… for evaluating multidocument summaries, we investigate unsupervised evaluation methods,
which … In particular, we focus on evaluating the relevance (Peyrard, 2019) of multi-document …

… As robustly capturing these statistical dependencies is a crucial step of the evaluation
metrics that do not rely on interventions, we argue that future work on disentanglement scores …

… unsupervised evaluation metrics. To do so, we leverage similarities and synergies between
evaluation metric … In particular, we use an unsupervised evaluation metric to mine pseudo-…

… in unsupervised learning makes the question of evaluation and cluster quality assessment
more complicated than in supervised learning. So … of the features and metrics used during the …

… the model parameters through Reinforcement Learning (RL) techniques. This makes the
choice of a good evaluation metric even more important. Unfortunately, ROUGE is known to …