You Are What You Annotate: Towards Better Models through Annotator Representations (2023)

Abstract: Annotator disagreement is ubiquitous in natural language processing (NLP) tasks. There are multiple reasons for such disagreements, including the subjectivity of the task, difficult cases, unclear guidelines, and so on. Rather than simply aggregating labels to obtain data annotations, we instead try to directly model the diverse perspectives of the annotators, and explicitly account for annotators’ idiosyncrasies in the modeling process by creating representations for each annotator (*annotator embeddings*) and also their annotations (*annotation embeddings*). In addition, we propose **TID-8**, **T**he **I**nherent **D**isagreement - **8** dataset, a benchmark that consists of eight existing language understanding datasets that have inherent annotator disagreement. We test our approach on TID-8 and show that our approach helps models learn significantly better from disagreements on six different datasets in TID-8 while increasing model size by fewer than 1% parameters. By capturing the unique tendencies and subjectivity of individual annotators through embeddings, our representations prime AI models to be inclusive of diverse viewpoints.

## introduction annotator disagreement is a common challenge in nlp @xcite . the conventional approach to reconciling such disagreements is to assume there is a single ground-truth label, and aggregate annotator labels on the same data instance @xcite . however, disagreement among annotators can arise from various factors, including differences in interpretation, certain preferences (e.g. due to annotators' upbringing or ideology), difficult cases (e.g., due to uncertainty or ambiguity), or multiple plausible answers @xcite . it is problematic to simply treat disagreements as noise and reconcile the disagreements by aggregating different labels into a single one. to illustrate, consider the case of hate speech detection, where certain humor text a: being crushed by large objects can be very depressing. text b: as you make your bed, so you will sleep on it. ann which is funnier, x means a tie: a, a, b, x, x to account for these disagreements, one approach is to directly learn from the data that has annotation disagreements @xcite , but representing this information inside the models is often not trivial. instead, to leverage the diverse viewpoints brought by different annotators, we create representations for the annotators (annotator embeddings) and for their annotations (annotation embeddings), with learnable matrices associated with both of these embeddings (see section 4). on downstream tasks, we forward the weighted embed-dings together with the text embeddings to the classification model, which adjusts its prediction for each annotator. intuitively, by modeling each annotator with a unique embedding, we accommodate their idiosyncrasies. by modeling the annotations themselves, we capture annotators' tendencies and views for individual annotation items. to test our methods, we propose tid-8, the inherent disagreement -8 dataset, a benchmark that consists of eight existing language understanding datasets that have inherent annotator disagreement. tid-8 covers the tasks of nli, sentiment analysis, hate speech detection, and humorousness comparison. empirical results on tid-8 show that annotator embeddings improve performance on tasks where individual differences such as the sense of humor matter, while annotation embeddings give rise to clusters, suggesting that annotation embeddings aggregate annotators with similar annotation behaviors. our approach helps models learn significantly better from disagreements on six datasets in tid-8 and yields a performance gain between 4%∼17% on four datasets that contain more than 50 annotators, while adding fewer than 1% of model parameters. we also conduct an ablation study and a comparison of the two embeddings over different datasets. by building and analyzing embeddings specific to the viewpoints of different annotators, we highlight the importance of considering annotator and annotation preferences when constructing models on data with disagreement. we hope to contribute towards democratizing ai by allowing for the representation of a diverse range of perspectives and experiences. in summary, our contributions include: tid-8 is publically available on huggingface at https://huggingface.co/datasets/ dnaihao/tid-8 . our code and implementation are available at https://github.com/michigannlp/ annotator-embeddings . researchers have criticized the conventional approach of assuming a single ground truth and ignoring the inherent annotator disagreement @xcite . various studies reveal that there exists genuine human variation in labeling because of the subjectivity of the task or multiple plausible answers @xcite @xcite @xcite . for instance, in the task of toxic language detection, not all text is equally toxic for everyone @xcite . the identities and beliefs of the annotator influence their view toward the toxic text @xcite . therefore, such annotator disagreement should not be simply dismissed as annotation "noise" @xcite . recently, researchers have started to leverage the different labels from annotators to better personalize the model for various users @xcite . ## task setup for the dataset d, where @xmath0, y i , a i ) e @xmath1, x i represents the input text, y i represents the corresponding label assigned by the annotator a i for text x i . the dataset d consists of e examples annotated by n unique annotators. we aim to optimize the model parameters θ to maximize the likelihood of the correct labels given the annotator a i and all of their input text x i : ## methods to explicitly account for annotation idiosyncrasies, we propose to create representations for both annotators and annotations. we create two embeddings, annotator embeddings (e a ), and annotation embeddings (e n ), associated with two learnable matrices (α a , α n ), respectively as shown in figure 1 . for the annotator embeddings, we assign each annotator a unique embedding that represents their individual annotating preferences. for the annotation embeddings, we first assign embeddings to each label in the dataset. we then take the average embedding of the labels annotated by an annotator on other examples as their annotation embedding. the intuition is that an annotator's labels on other examples can be viewed as a proxy of their annotation tendencies when they annotate the current example. we describe the two embedding methods in detail below. ## tid-8 overview we propose tid-8: the inherent disagreement -8 dataset. tid-8 consists of eight publicly available classification datasets with inherent annotator disagreement. in addition, information on the association between annotators and labels is available for all the datasets in tid-8. tid-8 covers the tasks of natural language inference (nli), sentiment and emotion classification, hate speech detection, and humorousness comparison. ## experiment setup in this paper, we investigate the setting of training models to directly learn from data that has inherent annotator disagreements. therefore, instead of aggregating the labels, we consider each annotation as a separate example. in other words, different labels may exist for the same text annotated by different annotators. ## results and discussion both proposed embeddings help them better learn from disagreement. despite increased performance, annotator and annotation embeddings have different effects. in scaling models up does not help models learn from disagreement. on the many annotators datasets in tid-8, we observe little to no performance difference between the base and large versions of each model. in some cases, the large models even underperform the base model, as seen with bert on the go emotions (goe) dataset. this result suggests that the increased capacity of the larger models does not necessarily translate into improved performance when dealing with datasets that exhibit significant annotation disagreement. however, when there is minimal disagreement, as observed in the friends qia (fia) dataset where only four examples have multiple labels while the remaining 5.6k examples have a single label (as shown in the superior performance of the larger models in low-disagreement scenarios suggests that they excel at capturing and leveraging subtle patterns present in the data. however, when faced with datasets that contain significant disagreement, the larger models may become more prone to overfitting. their increased capacity and specificity to the training data might hinder their ability to generalize well to new or unseen examples, resulting in diminished performance. models' ability to learn from disagreements is similar for text-only baselines but varies with different embeddings. for many annotators datasets in tid-8, we observe similar performances across models for text-only baselines. this suggests that these models possess a comparable text: we know it anecdotally from readers we've heard from who've been blatantly discriminated against because they're older. ability to learn from data that exhibits disagreements. however, the performance of these models varies when we incorporate annotation or annotator embeddings. this indicates that different pretraining strategies might have an impact on the effectiveness of incorporating annotator or annotation embeddings into a given model. apart from these analyses, we discuss performance patterns on each dataset in appendix c.5. ## further analyses since we observe a similar pattern across different transformer-based models, we use the bert base model for ablation and discussion in this section. ## conclusion instead of aggregating labels, we introduce a setting where we train models to directly learn from datasets that have inherent disagreements. we propose tid-8, the inherent disagreement -8 dataset, consisting of eight language understanding tasks that have inherent annotator disagreement. we introduced a method for explicitly accounting for annotator idiosyncrasies through the incorporation of annotation and annotator embeddings. our results on tid-8 show that integrating these embeddings helps the models learn significantly better from data with disagreements, and better accommodates individual differences. furthermore, our approach provides insights into differences in annotator perspectives and has implications for promoting more inclusive and diverse perspectives in nlp models. we hope that tid-8 and our approach will inspire further research in this area and contribute to the development of more effective and inclusive nlp methods.