Neural coreference resolution models trained on one dataset may not transfer to new, lowresource domains.Active learning mitigates this problem by sampling a small subset of data for annotators to label.While active learning is well-defined for classification tasks, its application to coreference resolution is neither well-defined nor fully understood.This paper explores how to actively label coreference, examining sources of model uncertainty and document reading costs.We compare uncertainty sampling strategies and their advantages through thorough error analysis.In both synthetic and human experiments, labeling spans within the same document is more effective than annotating spans across documents.The findings contribute to a more realistic development of coreference resolution models.

CSCW researchers have long inquired into the ways that identity informs, and is informed by, the design of technological systems. Gender is a regularly considered aspect of identity, with extensive work documenting and exploring gendered experiences and designs with the aim of addressing inequalities in, or through, design. Recent work has questioned the way that we conceptualise and "measure" gender, advocating more nuanced classificatory schemes to avoid silencing or obscuring trans and/or non-binary experiences. Building on and extending this research, our work examines how gender is conceptualised more broadly. Drawing from a range of theoretical perspectives in gender studies, feminist and postcolonial theory, we argue for the treatment of gender as "multiplicitous" when we conceptualise and interpret research, in order to avoid unintentionally perpetuating silencing and inequality even as we work to tackle it. Illustrating our argument with examples from both within and without CSCW, we suggest both new research directions for CSCW scholars inquiring into gender, and sensitising questions that scholars can use when constructing and evaluating studies.

We compare the multinomial i-vector framework from the speech community with LDA, SAGE, and LSA as feature learners for topic ID on multinomial speech and text data.We also compare the learned representations in their ability to discover topics, quantified by distributional similarity to gold-standard topics and by human interpretability.We find that topic ID and topic discovery are competing objectives.We argue that LSA and i-vectors should be more widely considered by the text processing community as pre-processing steps for downstream tasks, and also speculate about speech processing tasks that could benefit from more interpretable representations like SAGE.