### Related Work
A recent evaluation of ChatGPT on this dataset reported only 26% of that [29]. On the SemEval 2018 dataset, recent work reports a 0.60 macro-averaged F1 using RoBERTa [30] over the 11 emotions plus neutral, which was an improvement compared to the previous best-reported results such as SpanEmo's 0.58 F1 [31]. We also choose RoBERTa as our model and report a very similar F1 score of 0.59 over our 38 emotions, which is impressive considering it is a semi-supervised dataset with more fine-grained labels than previous datasets. The main method we use for building our semi-supervised dataset is ranking through a model trained on NLI. The viability of NLI based Zero-Shot classification to provide labels for emotion classification was established in Ref. [19], with model ensembles being more thoroughly covered in Ref. [32] and multiple templates in Ref. [33]. Both focused on the evaluation of NLI on existing emotion datasets, while this work focuses on creating a new dataset. We use two hypotheses for each emotion, where the main difference between them is the label rather than the template, which is similar to some prompts explored in Ref. [33]. Both of these previous works focused primarily on coarse-grained emotions, and evaluation of the Zero-shot classification, while the primary focus of ours is on fine-grained emotions and creating a new dataset. Zero-shot fine-grained emotion classification was done in Ref. [34] but only to serve as input to a sentiment classifier rather than focus directly on the emotion classification task. Related work has also shown that results of NLI based Zero-shot text classification can be improved in different ways by using ensembles of different hypothesis [33], ensembles of different models, and moving from zero-shot to few-shot [32]. Interestingly, fine-tuning pretrained NLI models for Zero-shot emotion classification can also be done without labeling data by using self-training [35]. In that work, the authors evaluate the entailment accuracy of their self-training approach on GoEmotions and conclude that it significantly improves results over the base NLI models. The key difference between our work and the related work is that rather than evaluate NLI Zero-Shot based approach on existing datasets, we use it to build a multi-label fine-grained dataset and evaluate fully supervised models trained on it. Soft labeling data refers to assigning each instance a discrete probability, such as a likelihood or confidence, that it belongs to each class. In comparison, when using hard or crisp labels, instances are assigned to a class with a probability of 1 (a certain event in probability theory). Soft labels can be captured from human annotators [36] but are more common in semi-supervised approaches [37], where they are often a part of a self-training, co-training process, label propagation, or from derived a pseudo-labeling classifier. Soft labels can be converted to hard labels, usually by thresholding at a certain probability. Commonly, if an example has a probability greater than 0.5 of belonging to a class, it is assigned that class label when converting to hard labels. Soft labels are often credited with better results than hard labels, attributed to proving robustness to noise and implicit regularization (e.g., Refs. [38; 39; 40; 41]). In this work, we will compare the use of soft and hard labels on our dataset. The key differences between our work and previous works that introduced new multi-label emotion datasets [12; 13; 26] can be summarized as follows:  
* Our work focuses on introducing a semi-supervised approach instead of crowdsourced workers for annotating training data;
* We introduce a dataset with more fine-grained emotions (38 labels) compared to previous datasets (11, 27, or 8 labels);
* Our work introduces the first multi-label or fine-grained emotion dataset for literature.