R edwood NLP at S em E val-2021 Task 7: Ensembled Pretrained and Lightweight Models for Humor Detection (2021)

Abstract: An understanding of humor is an essential component of human-facing NLP systems. In this paper, we investigate several methods for detecting humor in short statements as part of Semeval-2021 Shared Task 7. For Task 1a, we apply an ensemble of fine-tuned pre-trained language models; for Tasks 1b, 1c, and 2a, we investigate various tree-based and linear machine learning models. Our final system achieves an F1-score of 0.9571 (ranked 24 / 58) on Task 1a, an RMSE of 0.5580 (ranked 18 / 50) on Task 1b, an F1-score of 0.5024 (ranked 26 / 36) on Task 1c, and an RMSE of 0.7229 (ranked 45 / 48) on Task 2a.

## introduction humor detection is the process of identifying sequences of text that are amusing-an important task, as such sequences are present in most channels of communication. although humor detection comes naturally to humans, it is difficult for artificial systems to do the same. part of the challenge is that it is debatable what constitutes humor; what one reader finds funny may be found utterly prosaic by the next. the problem is only complicated when demographic factors come into play; now, the element of offense is also a factor. semeval-2021 shared task 7 attempts to address some of these open problems @xcite . rather than definitively labeling text as humorous or not, task 1a aims to determine whether the author intended for the sentence to be humorous, task 1b predicts its humor rating by the average user (its first moment), and task 1c attends to whether the variance of its humor ratings (its second moment) exceeds the median. task 2a, meanwhile, considers the text's average offensiveness score, a metric that often correlates with whether the author meant the text to be humorous and-perhaps equally importantly-affects whether the joke would be consid-ered acceptable. overall, training models to perform well on these tasks is of central importance to developing systems that are responsive to a wide range of input, whether in complete jest or meant to be taken at face value. ## dataset we train and validate our models on the semeval-2021 task 7 training set ( ## conclusion we have presented models trained to predict various aspects of humor in text: the level of intended humor, the level of humor for average users, and the level of controversy and offense of a given humorous statement. we find that large pretrained models such as electra, roberta, and bert are effective at predicting the level of intended humor. furthermore, we note that ensembling these models slightly improves performance. however, our ex-