  Coreference Resolution is a well studied problem in NLP. While widely studied
for English and other resource-rich languages, research on coreference
resolution in Bengali largely remains unexplored due to the absence of relevant
datasets. Bengali, being a low-resource language, exhibits greater
morphological richness compared to English. In this article, we introduce a new
dataset, BenCoref, comprising coreference annotations for Bengali texts
gathered from four distinct domains. This relatively small dataset contains
5200 mention annotations forming 502 mention clusters within 48,569 tokens. We
describe the process of creating this dataset and report performance of
multiple models trained using BenCoref. We expect that our work provides some
valuable insights on the variations in coreference phenomena across several
domains in Bengali and encourages the development of additional resources for
Bengali. Furthermore, we found poor crosslingual performance at zero-shot
setting from English, highlighting the need for more language-specific
resources for this task.
