  Coreference Resolution is an important NLP task and most state-of-the-art
methods rely on word embeddings for word representation. However, one issue
that has been largely overlooked in literature is that of comparing the
performance of different embeddings across and within families in this task.
Therefore, we frame our study in the context of Event and Entity Coreference
Resolution (EvCR & EnCR), and address two questions : 1) Is there a trade-off
between performance (predictive & run-time) and embedding size? 2) How do the
embeddings' performance compare within and across families? Our experiments
reveal several interesting findings. First, we observe diminishing returns in
performance with respect to embedding size. E.g. a model using solely a
character embedding achieves 86% of the performance of the largest model (Elmo,
GloVe, Character) while being 1.2% of its size. Second, the larger model using
multiple embeddings learns faster overall despite being slower per epoch.
However, it is still slower at test time. Finally, Elmo performs best on both
EvCR and EnCR, while GloVe and FastText perform best in EvCR and EnCR
respectively.
