# Annotation Curricula to Implicitly Train Non-Expert Annotators arXiv: https://arxiv.org/abs/2106.02382 ยท HTML: https://mlsanity.com/paper/2106.02382/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 96 references that are in the map. - First posted on arXiv 4 Jun 2021. ## Builds on (most cited first, 27 of 27) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019) - [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019) - [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015) - [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018) - [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015) - [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020) - [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018) - [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017) - [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://mlsanity.com/paper/1910.14599/) (2019) - [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019) - [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/1906.08158/) (2019) - [WinoGrande: An Adversarial Winograd Schema Challenge at Scale](https://mlsanity.com/paper/1907.10641/) (2019) - [Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets](https://mlsanity.com/paper/1908.07898/) (2019) - [To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks](https://mlsanity.com/paper/1903.05987/) (2019) - [Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study](https://mlsanity.com/paper/1808.05697/) (2018) - [Practical Obstacles to Deploying Active Learning](https://mlsanity.com/paper/1807.04801/) (2018) - [Text Readability Assessment for Second Language Learners](https://mlsanity.com/paper/1906.07580/) (2019) - [Changing the World by Changing the Data](https://mlsanity.com/paper/2105.13947/) (2021) - [Linguistic Features for Readability Assessment](https://mlsanity.com/paper/2006.00377/) (2020) - [Active Learning from Weak and Strong Labelers](https://mlsanity.com/paper/1510.02847/) (2015) - [On the definition of a confounder](https://mlsanity.com/paper/1304.0564/) (2013) - [Investigating label suggestions for opinion mining in German Covid-19 social media](https://mlsanity.com/paper/2105.12980/) (2021) - [Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains](https://mlsanity.com/paper/1906.02564/) (2019) - [Empowering Active Learning to Jointly Optimize System and User Demands](https://mlsanity.com/paper/2005.04470/) (2020) - [Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction](https://mlsanity.com/paper/1905.07791/) (2019) - [Manipulating the Difficulty of C-Tests](https://mlsanity.com/paper/1906.06905/) (2019) ## Built on by (most cited first, 8 of 8) - [Efficient Methods for Natural Language Processing: A Survey](https://mlsanity.com/paper/2209.00099/) (2022) - [LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework](https://mlsanity.com/paper/2112.07522/) (2021) - [Lessons Learned from a Citizen Science Project for Natural Language Processing](https://mlsanity.com/paper/2304.12836/) (2023) - [FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers' Preference Elicitation](https://mlsanity.com/paper/2403.06267/) (2024) - [Generative AI for Managerial Decision-Making under Ambiguity and Sycophancy](https://mlsanity.com/paper/2603.03970/) (2026) - [Evaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data Annotation](https://mlsanity.com/paper/2603.26592/) (2026) - [How Annotation Trains Annotators: Competence Development in Social Influence Recognition](https://mlsanity.com/paper/2604.02951/) (2026) - [BEATS: Bootstrapping E-commerce Attribute Taxonomies for Search through Iterative Human-AI Collaboration](https://mlsanity.com/paper/2606.04909/) (2026) ## Abstract (arXiv) Annotation studies often require annotators to familiarize themselves with the task, its annotation scheme, and the data domain. This can be overwhelming in the beginning, mentally taxing, and induce errors into the resulting annotations; especially in citizen science or crowd sourcing scenarios where domain expertise is not required and only annotation guidelines are provided. To alleviate these issues, we propose annotation curricula, a novel approach to implicitly train annotators. Our goal is to gradually introduce annotators into the task by ordering instances that are annotated according to a learning curriculum. To do so, we first formalize annotation curricula for sentence- and paragraph-level annotation tasks, define an ordering strategy, and identify well-performing heuristics and interactively trained models on three existing English datasets. We then conduct a user study with 40 voluntary participants who are asked to identify the most fitting misconception for English tweets about the Covid-19 pandemic. Our results show that using a simple heuristic to order instances can already significantly reduce the total annotation time while preserving a high annotation quality. Annotation curricula thus can provide a novel way to improve data collection. To facilitate future research, we further share our code and data consisting of 2,400 annotations.