Sarcasm detection plays an important role in natural language processing as it can impact the performance of many applications, including sentiment analysis, opinion mining, and stance detection. Despite substantial progress on sarcasm detection, the research results are scattered across datasets and studies. In this paper, we survey the current state-of-the-art and present strong baselines for sarcasm detection based on BERT pre-trained language models. We further improve our BERT models by fine-tuning them on related intermediate tasks before fine-tuning them on our target task. Specifically, relying on the correlation between sarcasm and (implied negative) sentiment and emotions, we explore a transfer learning framework that uses sentiment classification and emotion detection as individual intermediate tasks to infuse knowledge into the target task of sarcasm detection. Experimental results on three datasets that have different characteristics show that the BERT-based models outperform many previous models.

The recognition of dataset names is a critical task for automatic information extraction in scientific literature, enabling researchers to understand and identify research opportunities. However, existing corpora for dataset mention detection are limited in size and naming diversity. In this paper, we introduce the Dataset Mentions Detection Dataset (DMDD), the largest publicly available corpus for this task. DMDD consists of the DMDD main corpus, comprising 31,219 scientific articles with over 449,000 dataset mentions weakly annotated in the format of in-text spans, and an evaluation set, which comprises 450 scientific articles manually annotated for evaluation purposes. We use DMDD to establish baseline performance for dataset mention detection and linking. By analyzing the performance of various models on DMDD, we are able to identify open problems in dataset mention detection. We invite the community to use our dataset as a challenge to develop novel dataset mention detection models.

Qi Zhang, Zhijia Chen, Huitong Pan, Cornelia Caragea, Longin Jan Latecki, Eduard Dragut. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

The goal of zero-shot stance detection (ZSSD) is to identify the stance (in favor of, against, or neutral) of a text towards an unseen target in the inference stage. In this paper, we explore this problem from a novel angle by proposing a Target-based Teacher-Student learning (TTS) framework. Specifically, we first augment the training set by extracting diversified targets that are unseen during training with a keyphrase generation model. Then, we develop a teacher-student framework which effectively utilizes the augmented data. Extensive experiments show that our model significantly outperforms state-of-the-art ZSSD baselines on the available benchmark dataset for this task by 8.9% in macro-averaged F1. In addition, previous ZSSD requires human-annotated targets and labels during training, which may not be available in real-world applications. Therefore, we go one step further by proposing a more challenging open-world ZSSD task: identifying the stance of a text towards an unseen target without human-annotated targets and stance labels. We show that our TTS can be easily adapted to the new task. Remarkably, TTS without human-annotated targets and stance labels even significantly outperforms previous state-of-the-art ZSSD baselines trained with human-annotated data. We publicly release our code 1 to facilitate future research.

Henry Zou, Cornelia Caragea. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

Henry Zou, Vinay Samuel, Yue Zhou, Weizhi Zhang, Liancheng Fang, Zihe Song, Philip Yu, Cornelia Caragea. Findings of the Association for Computational Linguistics ACL 2024. 2024.