Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks (2022)

Abstract: Labelled data is the foundation of most natural language processing tasks. However, labelling data is difficult and there often are diverse valid beliefs about what the correct data labels should be. So far, dataset creators have acknowledged annotator subjectivity, but rarely actively managed it in the annotation process. This has led to partly-subjective datasets that fail to serve a clear downstream use. To address this issue, we propose two contrasting paradigms for data annotation. The descriptive paradigm encourages annotator subjectivity, whereas the prescriptive paradigm discourages it. Descriptive annotation allows for the surveying and modelling of different beliefs, whereas prescriptive annotation enables the training of models that consistently apply one belief. We discuss benefits and challenges in implementing both paradigms, and argue that dataset creators should explicitly aim for one or the other to facilitate the intended use of their dataset. Lastly, we conduct an annotation experiment using hate speech data that illustrates the contrast between the two paradigms.

## introduction many natural language processing (nlp) tasks are subjective, in the sense that there are diverse valid beliefs about what the correct data labels should be. some tasks, like hate speech detection, are highly subjective: different people have very different beliefs about what should or should not be labelled as hateful @xcite @xcite , and while some beliefs are more widely accepted than others, there is no single objective truth. other examples include toxicity @xcite , harassment @xcite , harmful content @xcite and stance detection @xcite as well as sentiment analysis @xcite . but even for seemingly objective tasks like part-of-speech tagging, there is subjective disagreement between annotators @xcite . in this article, we argue that dataset creators should consider the role of annotator subjectivity in the annotation process and either explicitly encourage it or discourage it. annotators may subjectively disagree about labels (e.g., for hate speech) but dataset creators can and should decide, based on the intended downstream use of their dataset, whether they want to a) capture different beliefs or b) encode one specific belief in their data. as a framework, we propose two contrasting data annotation paradigms. each paradigm facilitates a clear and distinct downstream use. the descriptive paradigm encourages annotator subjectivity to create datasets as granular surveys of individual beliefs. descriptive data annotation thus allows for the capturing and modelling of different beliefs. the prescriptive paradigm, on the other hand, discourages annotator subjectivity and instead tasks annotators with encoding one specific belief, formulated in the annotation guidelines. prescriptive data annotation thus enables the training of models that seek to consistently apply one belief. a researcher may, for example, want to model different beliefs about hate speech (→ descriptive paradigm), while a content moderation engineer at a social media company may need models that apply their content policy (→ prescriptive paradigm). neither paradigm is inherently superior, but explicitly aiming for one or the other is beneficial because it makes clear what an annotated dataset can and should be used for. for example, data annotated under the descriptive paradigm can provide insights into different beliefs ( §2.1), but it cannot easily be used to train models with one pre-specified behaviour ( §3.1). by contrast, leaving annotator subjectivity unaddressed, as has mostly been the case in nlp so far, leads to datasets that neither capture an interpretable diversity of beliefs nor consistently encode one specific belief; an undesirable middle ground without a clear downstream use. 1 the two paradigms are applicable to all data annotation. they can be used to compare existing datasets, and to make and communicate decisions about how new datasets are annotated as well as how annotator disagreement can be interpreted. we hope that by naming and explaining the two paradigms, and by discussing key benefits and challenges in their implementation, we can support more intentional annotation process design, which will result in more useful nlp datasets. terminology our use of the terms descriptive and prescriptive aligns with their use in both linguistics and ethics. in linguistics, descriptivism studies how language is used, whereas prescriptive grammar declares how language should be used @xcite . descriptive ethics studies the moral judgments that people make, while prescriptive ethics considers how people ought to act @xcite . accordingly, descriptive data annotation surveys annotators' beliefs, whereas prescriptive data annotation aims to encode one specific belief, which is formulated in the annotation guidelines. ## the descriptive annotation paradigm: as toxic @xcite , and that people who identify as lgbtq+ or young adults are more likely to rate random social media comments as toxic @xcite . similar correlations between sociodemographic characteristics and annotation outcomes have been found in stance @xcite , sentiment @xcite and hate speech detection @xcite . even very subjective tasks may have clear-cut entries on which most annotators agree. for example, crowd workers tend to agree more on the extremes of a hate rating scale @xcite , and datasets which consist of clear hate and non-hate can have very high levels of inter-annotator agreement, even with minimal guidelines @xcite . descriptive data annotation can help to identify which entries are more subjective. jiang et al. @xcite , for instance, find that perceptions about the harmfulness of sexually explicit language vary strongly across the eight countries in their sample, whereas support for mass murder or human trafficking is seen as very harmful across all countries. learning from disagreement annotator-level labels from descriptive data annotation have been shown to be a rich source of information for model training. first, they can be used to separately model annotators' beliefs. for less subjective tasks such as question answering, this has served to mitigate undesirable annotator biases @xcite . @xcite reframe and expand on this idea for more subjective tasks like abuse detection, showing that multi-annotator model architectures outperform standard single-label approaches on single label prediction. second, instead of modelling each annotator separately, other work has grouped them into clusters based on sociodemographic attributes @xcite or polarisation measures derived from annotator labels @xcite , with similar results. third, models can be trained directly on soft labels (i.e., distributions of labels given by annotators), rather than hard one-hot ground truth vectors @xcite @xcite . evaluating with disagreement descriptive data annotation facilitates model evaluation that accounts for different beliefs about how a model should behave @xcite . this is particularly relevant when deploying nlp systems for practical tasks such as content moderation, where user-facing performance needs to be considered @xcite . to this end, comparing a model prediction to a descriptive label distribution, the crowd truth @xcite , can help estimate how acceptable the prediction would be to users @xcite . @xcite operationalise this idea by introducing jury learning, a recommender system approach to predicting how a group of annotators with specified sociodemographic characteristics would judge different pieces of content. ## an illustrative annotation experiment experimental design to illustrate the contrast between the two paradigms, we conducted an annotation experiment. 60 annotators were randomly assigned to one of three groups of 20. each group was given different guidelines to label the same 200 twitter posts, taken from a corpus annotated for hate speech by @xcite , as either hateful or non-hateful. g1, the descriptive group, received a short prompt which directed them to apply their subjective judgement ('do you personally feel this post is hateful?'). g2, the prescriptive group, received a short prompt which discouraged subjectivity ('does this post meet the criteria for hate speech?'), along with detailed annotation guidelines. g3, the control group, received the prescriptive prompt and a short definition of hate speech but no further guidelines. this is to control for the difference in length and complexity of annotation guidelines between g1 and g2. ## conclusion in this article, we named and explained two contrasting paradigms for data annotation. the descriptive paradigm encourages annotator subjectivity to create datasets as granular surveys of individual beliefs, which can then be analysed and modelled. the prescriptive paradigm tasks annotators with encoding one specific belief formulated in the annotation guidelines, to enable the training of models that seek to apply that one belief to unseen data. dataset creators should explicitly aim for one paradigm or the other to facilitate the intended downstream use of their dataset, and to document for the benefit of others how exactly their dataset was annotated. we discussed benefits and challenges in implementing both paradigms, and conducted an annotation experiment that illustrates the contrast between them. we hope that the two paradigms can support more intentional annotation process design and thus facilitate the creation of more useful nlp datasets.