hub at S em E val-2021 Task 7: Fusion of ALBERT and Word Frequency Information Detecting and Rating Humor and Offense (2021)

Abstract: This paper introduces the system description of the hub team, which explains the related work and experimental results of our team’s participation in SemEval 2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense. We successfully submitted the test set prediction results of the two subtasks in the task. The goal of the task is to perform humor detection, grade evaluation, and offensive evaluation on each English text data in the data set. Tasks can be divided into two types of subtasks. One is a text classification task, and the other is a text regression task. What we need to do is to use our method to detect the humor and offensive information of the sentence as accurately as possible. The methods used in the results submitted by our team are mainly composed of ALBERT, CNN, and Tf-Idf algorithms. The result evaluation indicators submitted by the classification task are F1 score and Accuracy. The result evaluation index of the regression task submission is the RMSE. The final scores of the prediction results of the two subtask test sets submitted by our team are task1a 0.921 (F1), task1a 0.9364 (Accuracy), task1b 0.6288 (RMSE), task1c 0.5333 (F1), task1c 0.0.5591 (Accuracy), and task2 0.5027 (RMSE) respectively.

## introduction and background perceiving humor has always been a unique ability of human beings. so what is the use of humor? the research results of martin and kuiper on humor show us the influence of humor on a person's physical and mental health @xcite . in recent years, the use of automated methods to detect humorous information in text has attracted widespread attention @xcite . semeval 2021 task 7: ha-hackathon: detecting and rating humor and offense's task goal is to use automated techniques and methods to automatically detect humor and grade in the text. besides, this task also needs to evaluate the offensive level of the text data. the task is divided into two parts, one part is the detection and evaluation related to humor. there are three subtasks in this part of the task. it involves text classification and regression. the other part is to assess the offensive level of the text data. this is a separate text regression task. the purpose is to predict how offensive the text will be to ordinary users. this task is an interesting challenge for the machine. humor is a very subjective emotion. people with different cultural backgrounds and life experiences have different feelings about the same sentence. this task is to detect and score humor on the english data set. there are similar tasks and studies in other languages, such as humor scores on spanish data from tweets @xcite . in text detection and classification tasks, semisupervised and supervised methods are widely used. davidov et al. use a semi-supervised method to detect text from social media. the purpose is to detect whether the text data contains ironic information @xcite . but these methods alone are not enough to make humor ratings on text data. we need to combine semantic information in context. the elmo @xcite ) method based on lstm @xcite overcomes the difficulty that the model cannot learn the context. in the followup work, an improved method of elmo feature extractor appeared. the bert @xcite model based on transformer encoder @xcite achieved the best results in many nlp tasks. ## data and methods in this section, we will introduce the data we use in the task and the models and methods we use. ## experiment and results in this section, we will introduce the data preprocessing methods and experimental settings we used in the task and the final results. ## results and analysis the evaluation indicators announced by the task organizer team in this task are divided into classification tasks and regression tasks. the classification task uses f1 scores and accuracy scores. the regression task uses root mean squared error (rmse). we compare the results obtained by several different methods proposed in the experimental part. the scores of different systems on the same validation set are shown in the ranking of the test set prediction results announced by the task organizer team uses accuracy scores and rmse scores respectively. classification tasks are ranked according to accuracy scores. regression tasks are ranked according to rmse scores. we finally submitted the test set prediction result score from the albert+tf-idf+cnn system. because its combined result in training time and score is better than the bert+tf-idf+cnn system. the prediction result scores of the test set we submitted can be seen in ## conclusion in this task related to humor detection and offensive evaluation, we propose an artificial neural network system that combines a pre-trained language model with tf-idf and cnn. although our system is only in the middle of the ranking, we still successfully use our system to predict humor and offensive scores. we studied the contributions of 6 different systems and found that the combination of tf-idf and cnn improved the prediction scores of bert and albert. at the same time, the albert-based system is superior to the bertbased system in terms of time efficiency. in future work, based on the model we use in the task, we can try to fuse other types of word embedding information, and replace the cnn block with an lstm block or other artificial neural networks.