  Humor and Offense are highly subjective due to multiple word senses, cultural
knowledge, and pragmatic competence. Hence, accurately detecting humorous and
offensive texts has several compelling use cases in Recommendation Systems and
Personalized Content Moderation. However, due to the lack of an extensive
labeled dataset, most prior works in this domain haven't explored large neural
models for subjective humor understanding. This paper explores whether large
neural models and their ensembles can capture the intricacies associated with
humor/offense detection and rating. Our experiments on the SemEval-2021 Task 7:
HaHackathon show that we can develop reasonable humor and offense detection
systems with such models. Our models are ranked third in subtask 1b and
consistently ranked around the top 33% of the leaderboard for the remaining
subtasks.
