Though pre-trained language models achieve notable success in many applications, it’s usually controversial for over-confident predictions. Specifically, the in-distribution (ID) miscalibration and out-of-distribution (OOD) detection are main concerns. Recently, some works based on energy-based models (EBM) have shown great improvements on both ID calibration and OOD detection for images. However, it’s rarely explored in natural language understanding tasks due to the non-differentiability of text data which makes it more difficult for EBM training. In this paper, we first propose a triple-hybrid EBM which combines the benefits of classifier, conditional generative model and marginal generative model altogether. Furthermore, we leverage contrastive learning to approximately train the proposed model, which circumvents the non-differentiability issue of text data. Extensive experiments have been done on GLUE and six other multiclass datasets in various domains. Our model outperforms previous methods in terms of ID calibration and OOD detection by a large margin while maintaining competitive accuracy.

General domain Named Entity Recognition (NER) datasets like CoNLL-2003 mostly annotate coarse-grained location entities such as a country or a city. But many applications require identifying fine-grained locations from texts and mapping them precisely to geographic sites, e.g., a crossroad, an apartment building, or a grocery store. In this paper, we introduce a new dataset HarveyNER with fine-grained locations annotated in tweets. This dataset presents unique challenges and characterizes many complex and long location mentions in informal descriptions. We built strong baseline models using Curriculum Learning and experimented with different heuristic curricula to better recognize difficult location mentions. Experimental results show that the simple curricula can improve the system’s performance on hard cases and its overall performance, and outperform several other baseline systems. The dataset and the baseline models can be found at https://github.com/brickee/HarveyNER.