  HCI and NLP traditionally focus on different evaluation methods. While HCI
involves a small number of people directly and deeply, NLP traditionally relies
on standardized benchmark evaluations that involve a larger number of people
indirectly. We present five methodological proposals at the intersection of HCI
and NLP and situate them in the context of ML-based NLP models. Our goal is to
foster interdisciplinary collaboration and progress in both fields by
emphasizing what the fields can learn from each other.
