Sexism is one of the most concerning problems in the internet society. By detecting sexist expressions, we can reduce the offense toward females and provide useful information to understand how sexism occurs. Our work focuses on a newly-published dataset, EDOS, which annotates English sexist expressions from Reddit and categorizes their specific types. Our method is to train a DeBERTaV3 classifier with all three kinds of labels provided by the dataset, including sexist, category, and granular vectors. Our classifier predicts the probability distribution on vector labels and further applies it to represent category and sexist distributions. Our classifier uses its label and finer-grained labels for each classification to calculate the hierarchical loss for optimization. Our experiments and analyses show that using a combination of loss with finer-grained labels generally achieves better performance on sexism detection and categorization. Codes for our implementation can be found at https://github.com/KomeijiForce/SemEval2023_Task10.

Given the high-stakes nature of healthcare decision-making, we aim to improve the efficiency of human annotators rather than replacing them with fully automated solutions. We introduce a new comprehensive resource, SYMPTOMIFY, a dataset of annotated vaccine adverse reaction reports detailing individual vaccine reactions. The dataset, consisting of over 800k reports, surpasses previous datasets in size. Notably, it features reasoning-based explanations alongside background knowledge obtained via language model knowledge harvesting. We evaluate performance across various methods and learning paradigms, paving the way for future comparisons and benchmarking.

We study the problem of entity detection and normalization applied to patient self-reports of symptoms that arise as side-effects of vaccines. Our application domain presents unique challenges that render traditional classification methods ineffective: the number of entity types is large; and many symptoms are rare, resulting in a long-tail distribution of training examples per entity type. We tackle these challenges with an autoregressive model that generates standardized names of symptoms. We introduce a data augmentation technique to increase the number of training examples for rare symptoms. Experiments on real-life patient vaccine symptom self-reports show that our approach outperforms strong baselines, and that additional examples improve performance on the long-tail entities.

As research on utilizing human knowledge in natural language processing has attracted considerable attention in recent years, knowledge graph (KG) completion has come into the spotlight. Recently, a new knowledge graph completion method using a pre-trained language model, such as KG-BERT, is presented and showed high performance. However, its scores in ranking metrics such as Hits@k are still behind state-of-the-art models. We claim that there are two main reasons: 1) failure in sufficiently learning relational information in knowledge graphs, and 2) difficulty in picking out the correct answer from lexically similar candidates. In this paper, we propose an effective multi-task learning method to overcome the limitations of previous works. By combining relation prediction and relevance ranking tasks with our target link prediction, the proposed model can learn more relational properties in KGs and properly perform even when lexical similarity occurs. Experimental results show that we not only largely improve the ranking performances compared to KG-BERT but also achieve the state-of-the-art performances in Mean Rank and Hits@10 on the WN18RR dataset.