Computer Science
Computer Science-2021.gz:143357
Computer Science
57aef9a3786ae50a91f51cf5176061269eac4b3c
abcbpc at SemEval-2021 Task 7: ERNIE-based Multi-task Model for Detecting and Rating Humor and Offense

This paper describes our system participated in Task 7 of SemEval-2021: Detecting and Rating Humor and Offense. The task is designed to detect and score humor and offense which are influenced by subjective factors. In order to obtain semantic information from a large amount of unlabeled data, we applied unsupervised pre-trained language models. By conducting research and experiments, we found that the ERNIE 2.0 and DeBERTa pre-trained models achieved impressive performance in various subtasks. Therefore, we applied the above pre-trained models to fine-tune the downstream neural network. In the process of fine-tuning the model, we adopted multi-task training strategy and ensemble learning method. Based on the above strategy and method, we achieved RMSE of 0.4959 for subtask 1b, and finally won the first place.


Introduction
Humor, as a highly subjective phenomenon, can be affected by various factors. Automatic humor recognition relies on annotated data to determine whether the text is humorous or not (Mihalcea and Strapparava, 2005). However, such a binary classification does not capture the level of humor, so assessing the level of humor is of great significance (Garimella et al., 2020). Since humor can be influenced by many factors, such as age, and may offend others. Based on such a situation, SemEval-2021 Task 7 focuses on linking humor and offense across different age groups (Meaney et al., 2021). But there are still many challenges to this task. For example, the dataset for the task is small and the texts are short, which does not allow for adequate training. To address these issues, we utilized unsupervised pre-trained language models and fine-tuned these models for specific downstream subtasks. After conducting research and extensive comparative experiments, the results show that ERNIE 2.0 (Sun et al., 2019b) and DeBERTa pre-trained models performed best on the subtasks. These large unsupervised language models were pre-trained on a large amount of unlabeled data to extract valuable lexical, syntactic, and semantic information from the corpus. Vector representations of text computed by these models are applied to fine-tune the downstream neural networks for the subtasks. The multi-task training and ensemble learning method significantly improve our model's performance.
The rest of the paper is organized as follows: Section 2 provides a brief description of the related work, and Section 3 describes our proposed approach in detail. In Section 4, the experiments are described in detail and the results are presented. Finally, we summarize the whole paper and discuss future research directions in Section 5.

Related Work
In the early research of humor and offense detection and evaluation, traditional machine learning methods and n-gram language model were mostly used.
Recent research has shown that unsupervised language pre-trained models using large amounts of unlabeled data have achieved state-of-the-art results in a large number of natural language processing tasks. For example, BERT (Devlin et al., 2018) is a model built based on Transformer Encoder, which is used for downstream tasks by pre-training on the masked language models task and the next sentence prediction task, and then for fine-tuning. Inspired by this approach, many pre-training language models have been proposed. For example, ALBERT (Lan et al., 2019) adopts Factorized Embedding Parameterization and Cross-layer parameter sharing strategies, and adds the sentence order prediction task, so that the model can greatly Figure 1: Architecture of our model for multi-task training. The public section is shared by all subtasks, while the private section is subtask-specific. First, text is transformed into subwords, and ERNIE 2.0 generates the corresponding contextual vector representation. Then different loss functions are set for each subtask to generate task-specific representations. This is eventually used for classification and regression subtasks in different scenarios.
reduce the number of parameters without lose accuracy compared to BERT. RoBERTa (Liu et al., 2019b) removes the next sentence prediction task and uses dynamic MASK, which is optimized for BERT. DeBERTa (He et al., 2020) improves the BERT and RoBERTa models by using two new techniques. The first one is using Disentangled attention mechanism and secondly, Enhanced mask decoder is used. MT-DNN (Liu et al., 2019a) combines Multi-task Learning and pre-trained models to improve the performance of various natural language processing tasks. ERNIE 1.0 (Sun et al., 2019a) employs an entitylevel and phrase-level mask, the extension of its training corpus and the use of multiple rounds of conversation to replace sentence pair classification further enhance the model's semantic representation capability. ERNIE 2.0 (Sun et al., 2019b) is an optimized version of ERNIE 1.0, which introduces a large number of pre-training tasks and continuously updates the pre-training model through multi-task learning to help the model learn lexical, syntactic and semantic representations efficiently. ERNIE 2.0 constructs three pre-training tasks, namely word-aware pre-training tasks, structureaware pre-training tasks and semantic-aware pretraining tasks. The performance of the model is improved by constructing pre-training tasks from multiple perspectives. The ERNIE 2.0 model outperformed BERT and XLNet (Yang et al., 2019) almost across the board on the English task and achieved the best results on 7 GLUE tasks; on the Chinese task, the ERNIE 2.0 model outperformed BERT across the board on all 9 Chinese NLP tasks.

Multi-task training
To mitigate overfitting for specific tasks, we adopt a multi-task training strategy that combines pretrained language model and multi-task training (as shown in Figure 1). Based on the above strategy, it makes the learned representation generalizable across tasks and improves the performance of various downstream subtasks.
The architecture of our model is shown in Figure 1, with the pre-trained model ERNIE 2.0 as the public section, which is used for generating semantic information common to downstream tasks. and the multi-task training as the private section, where individual subtasks are trained to produce task-specific representations by using different loss functions.
When we fine-tune our model, the input to the model is the data from all subtasks. Words from the text in different subtasks are first processed by tokenizer to generate subwords. After the subwords are transformed into tokens by the mapping of the lexicon, the tokenized sentences are stitched together with [CLS] and [SEP] as the input to the ERNIE 2.0 model to obtain the contextual vector representation corresponding to each token. Multitask training is a fully-connected layer followed by the ERNIE 2.0 model, and the four downstream subtasks optimize the subtask-specific model by constructing different loss functions for gradient Figure 2: 8-fold cross-validation and ensemble. The training set is divided randomly for 8 times by setting different random seeds. In each division, the training set T is divided into 8 parts, of which 7 parts are respectively used as the training set and the remaining 1 part is used as the validation set. And finally the average of all saved best models predicted on the test set are the final results.
updating. For the classification task we first use the sigmod activation function to constrain the output between 0 and 1 before using the BCE loss function, and for the regression task we use the MSE loss function.

Ensemble
We adopt cross-validation for training as a way to improve the robustness of our model, as shown in Figure 2. We first divided the training set eight times by setting different random seeds. Therefore, 8 folds of data are generated, with 7000 training samples and 1000 validation samples in each fold. When fine-tune our model for each fold, the best model for each subtask at each fold of training is saved. For subtask 1a and subtask 1c, the evaluation metric is F1-Score, and for subtask 1b and subtask 2a the evaluation metric is RMSE. finally, we take the mean of all the best saved models after making predictions on the test set as the final results.

Experimental details
All of our experiments were run on the Nvidia Tesla V100. In order to obtain more valuable information from the limited training data and to reduce overfitting to some extent, we adopt the multi-task training strategy and ensemble learning method.
For the training of the per-fold model, we choose the Adam optimizer, set the epoch to 10, and use early stopping strategies according to the performance on the validation set. Considering the small amount of data in the training set, we set a smaller learning rate for the ERNIE 2.0 model layer and a larger learning rate for the fully connected layer where the subtasks are trained together. Specifically, when fine-tuning our model, we adopt the grid search strategy with the learning rate ranging from 2e-5 to 5e-5 and the batch size ranging from 32 to 48. Besides, we set the learning rate as a linear function and use a warm-up strategy in the training phase. The ensemble approach we adopt is mainly based on the average prediction results. The specific methods are as follows: for the classification subtask, the prediction probabilities of all base models in each category are averaged, and then the category with the highest probability is taken as the prediction result; while for the regression subtask, the prediction values of all base models are averaged as the final prediction result.

Comparison experiments
In order to verify the effectiveness of our proposed multi-task training strategy based on the ERNIE 2.0 model, we set up two comparison experiments. They are described as follows: (1) Comparison experiments of multi-task training together and single-task training separately based on ERNIE 2.0 model.
(2) Comparison experiments of single-task separate training based on ERNIE 2.0 and DeBERTa models.  the evaluation metrics of each subtask, The number of parameters in the ERNIE 2.0 model (425M) is less than the DeBERTa model (750M), but the ERNIE 2.0 pre-trained model performs better than the DeBERTa model on several subtasks. Besides, Compared to single-task training, the strategy of using multi-task training shows a significant improvement in performance. Moreover, The impact of ensemble learning method on improving model performance is significant.

Conclusion
In this paper, we proposed a multi-task training system based on ERNIE 2.0. We describe the architecture of the model and the training process in detail. Besides, we experimentally demonstrate that the strategy performs better with multi-task training compared to single-task training. Moreover, the ensemble learning method makes the model more robust. As a result, we have won the first place in a subtask for the competition of SemEval-2021 task 7. In our future work, we will further explore pretrained language model and optimize the multi-task training.