Abstract
This paper introduces the result of Team Grenzlinie’s experiment in SemEval-2021 task 7: HaHackathon: Detecting and Rating Humor and Offense. This task has two subtasks. Subtask1 includes the humor detection task, the humor rating prediction task, and the humor controversy detection task. Subtask2 is an offensive rating prediction task. Detection task is a binary classification task, and the rating prediction task is a regression task between 0 to 5. 0 means the task is not humorous or not offensive, 5 means the task is very humorous or very offensive. For all the tasks, this paper chooses RoBERTa as the pre-trained model. In classification tasks, Bi-LSTM and adversarial training are adopted. In the regression task, the Bi-LSTM is also adopted. And then we propose a new approach named compare method. Finally, our system achieves an F1-score of 95.05% in the humor detection task, F1-score of 61.74% in the humor controversy detection task, 0.6143 RMSE in humor rating task, 0.4761 RMSE in the offensive rating task on the test datasets.
Highlights
Humorous is one kind most interesting, most has the power, most has the universal significance transmission art
Pre-trained models (PTMs) can learn common language representation, which is beneficial for subsequent NLP tasks and can avoid training new models from scratch (Wang et al, 2018)
Baseline Model In these tasks, RoBERTa is adopted as the baseline model, and softmax is adopted as the activation function in the classification task
Summary
Humorous is one kind most interesting, most has the power, most has the universal significance transmission art. Humor is one of the ways to improve the quality of daily conversation. In the field of natural language processing, how to make the computer learn humor and improve the quality of human-computer interaction is an important problem. The previous researches task was only to input the humorous corpus into the deep learning network and let the algorithm learn how to generate humorous dialogue. In this case, the sentences are often problematic. Before the computer learns to generate humorous sentences, it is an important task for the computer to understand humor and distinguish different degrees and forms of humor.
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