Cross-Domain Hate Speech Detection: A Multitask Learning Approach

In the field of emotion recognition, researchers are exploring ways to train models on multiple datasets from different domains to improve their performance. However, this approach can lead to unintended consequences if not handled properly. In this article, we will delve into the challenges of cross-domain generalization in emotion recognition and discuss strategies to mitigate them.
Firstly, let’s define what cross-domain generalization means in this context. Essentially, it involves training an emotion recognition model on one dataset and expecting it to perform well on another dataset from a different domain. For instance, a model trained on a dataset of Twitter posts might be expected to work well on Reddit comments or Facebook status updates.
One of the main challenges in cross-domain generalization is that emotions can vary across domains due to differences in language, culture, and context. This means that a model trained on one domain may not capture the nuances of another domain, leading to poor performance. To address this issue, researchers have proposed various techniques, such as incorporating additional emotion knowledge or using transfer learning.
Another challenge is that some datasets might contain biases, which can affect the model’s ability to generalize across domains. For example, if a model is trained on a dataset with a bias towards explicit sexism, it may not perform well on other datasets with different biases. In such cases, using emotion knowledge from multiple sources can help mitigate these biases.
In addition to these challenges, there are also issues related to the quality and consistency of the datasets used for training. If the datasets are small or noisy, the model may not be able to learn useful representations, leading to poor performance across domains.
To overcome these challenges, researchers have proposed several strategies. One approach is to use transfer learning, where a pre-trained model is fine-tuned on a new dataset from another domain. This can help the model adapt to the new domain and improve its performance. Another approach is to incorporate additional emotion knowledge, such as that provided by 28-class emotion corpora like GEgo, which can help mitigate semantic variance across contrasting HS topics more than six-class GEek corpus.
In conclusion, cross-domain generalization in emotion recognition is a complex issue that requires careful consideration of the challenges involved. By using strategies such as transfer learning and incorporating additional emotion knowledge, researchers can improve the performance of their models across different domains. As the field continues to evolve, we can expect to see more innovative approaches to addressing these challenges and improving the accuracy of emotion recognition systems.