Aggregating and analysing crowdsourced annotations for NLP (AnnoNLP)

The workshop is hosted by EMNLP-IJCNLP 2019 which will take place in Hong Kong.

Workshop Description


Crowdsourcing, whether through microwork platforms or through Games with a Purpose, is increasingly used as an alternative to traditional expert annotation, achieving comparable annotation quality at lower cost and offering greater scalability. The NLP community has enthusiastically adopted crowdsourcing to support work in tasks such as coreference resolution, sentiment analysis, textual entailment, named entity recognition, word similarity, word sense disambiguation, and many others. This interest has also resulted in the organization of a number of workshops at ACL and elsewhere, from as early as “The People’s Web meets NLP” in 2009. These days, general purpose research on crowdsourcing can be presented at HCOMP or CrowdML, but the need for workshops more focused on the use of crowdsourcing in NLP remains. In particular, NLP-specific methods are typically required for the task of aggregating the interpretations provided by the annotators. Most existing work on aggregation methods is based on a common set of assumptions: 1) independence between the true classes, 2) the set of classes the coders can choose from is fixed across the annotated items, and 3) there is one true class per item. However, for many NLP tasks such assumptions are not entirely appropriate. For example, sequence labelling tasks (e.g., NER, tagging) have an implicit inter-label dependence (e.g., Nguyen et al., 2017). In other tasks such as coreference the labels the coders can choose from are not fixed but depend on the mentions from each document (Passonneau, 2004; Paun et al., 2018). Furthermore, in many NLP tasks, the data items can have more than one interpretation (e.g., Poesio and Artstein, 2005; Passonneau et al., 2012; Plank et al., 2014). Such cases of ambiguity also affect the reliability of existing gold standard datasets (often labelled with a single interpretation even though expert disagreement is a well-known issue). This former point motivates the research on alternative, complementary evaluation methods, but also the development of multi-label datasets. More broadly, the proposed workshop aims to bring together researchers interested in methods for aggregating and analysing crowdsourced data for NLP-specific tasks which relax the aforementioned assumptions. We also invite work on ambiguous, subjective or complex annotation tasks which received less attention in the literature.


Although there is a large body of work analysing crowdsourced data, be that probabilistic (models of annotation) or traditional (majority voting aggregation, agreement statistics), there has been less work devoted to NLP tasks. It is often the case that NLP data violate the assumptions made by most existing models, opening the path to new research. The aim of the proposed workshop is to bring together the community of researchers interested in this area.


Topics of interest include but are not limited to the following:

Important Dates

Submission Details

We welcome both long and short papers. Long papers are expected to have at most 8 pages of content, while short papers should have up to 4 content pages. Submissions should follow the EMNLP-IJCNLP 2019 guidelines. References do not count against these limits.

Papers should be submitted online via START.

Workshop Organizers

Silviu Paun, Queen Mary University of London,
Dirk Hovy, Bocconi University,

Invited Speakers

Jordan Boyd-Graber, University of Maryland

Edwin Simpson, Technische Universität Darmstadt

Programme Committee