Netherlands

Recently, Peterson et al. provided evidence of the benefits ofusing probabilistic soft labels generated from crowd annota-tions for training a computer vision model, showing that us-ing such labels maximizes performance of the models overunseen data. In this paper, we generalize these results byshowing that training with soft labels is an effective methodfor using crowd annotations in several otherAItasks besidesthe one studied by Petersonet al., and also when their perfor-mance is compared with that of state-of-the-art methods forlearning from crowdsourced data.

HCOMP 2020

A Case for Soft Loss Functions

soft loss

soft labels

soft evaluation

The 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2020) will be held October 26–28th virtually at The Netherlands Institute for Sound and Vision in Hilversum, The Netherlands.

While artificial intelligence (AI) and human-computer interaction (HCI) represent traditional mainstays of the conference, HCOMP believes strongly in fostering and promoting broad, interdisciplinary research. Our field is particularly unique in the diversity of disciplines it draws upon and contributes to, including human-centered qualitative studies and HCI design, social computing, artificial intelligence, economics, computational social science, digital humanities, policy, and ethics. We promote the exchange of advances in human computation and crowdsourcing not only among researchers, but also engineers and practitioners, to encourage dialogue across disciplines and communities of practice.

This year, we especially invite works that will generate new insights into the quality (or qualities) of human-annotated datasets, including elements such as reliability and replicability of human computation and crowdsourcing experiments, novel metrics for aggregation of results, and holistic approaches to deal with bias, fairness and interpretability.

The 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2020) was held October 26–28th virtually at The Netherlands Institute for Sound and Vision in Hilversum, The Netherlands.

While artificial intelligence (AI) and human-computer interaction (HCI) represent traditional mainstays of the conference, HCOMP believes strongly in fostering and promoting broad, interdisciplinary research. Our field is particularly unique in the diversity of disciplines it draws upon and contributes to, including human-centered qualitative studies and HCI design, social computing, artificial intelligence, economics, computational social science, digital humanities, policy, and ethics. We promote the exchange of advances in human computation and crowdsourcing not only among researchers, but also engineers and practitioners, to encourage dialogue across disciplines and communities of practice.

This year, we especially invite works that will generate new insights into the quality (or qualities) of human-annotated datasets, including elements such as reliability and replicability of human computation and crowdsourcing experiments, novel metrics for aggregation of results, and holistic approaches to deal with bias, fairness and interpretability.

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

technical paper

The verification of Extended Entity Relationship (EER) diagrams and other conceptual models that capture the design of information systems is crucial to ensure reliable systems. To scale up verification processes to larger groups of experts, Human Computation techniques were used focusing primarily on closed tasks, which constrain the number and variety of reported defects in favour of easy aggregation of derived judgements. To address this limitation of closed tasks, in this paper, we investigate EER verification with open tasks to extend the range of collected results. We also address the challenge of aggregating results of open tasks by proposing a follow-up HC task for defect validation. We evaluate our approach for HC-based EER Verification with open tasks in a set of experiments conducted with junior developers and show that (1) open tasks allow collecting a variety of insights that go beyond a manually built gold standard while still leading to good performance (F1=60%) and (2) HC-based validation can be reliably used for validating the results of open tasks (F1=84% compared to expert validation). 


Verifying Extended Entity Relationship Diagrams with Open Tasks

Image Tagging Algorithms (ITAs) are extensively used in our information ecosystem, from facilitating the retrieval of images in social platforms to learning about users and their preferences. However, audits performed on ITAs have demonstrated that their behaviors often exhibit social biases, especially when analyzing images depicting people. We present OpenTag, a platform that fuses the auditing process with a crowdsourcing approach. Users can upload an image, which is then analyzed by various ITAs, resulting in multiple sets of descriptive tags. With OpenTag, the user can observe and compare the output of multiple ITAs simultaneously, while researchers can study the manner in which users perceive this output. Finally, using the collected data, further audits can be performed on ITAs.

OpenTag: Understanding Human Perceptions of Image Tagging Algorithms

Qualification tests in crowdsourcing are often used to pre-filter workers by measuring their ability in executing microtasks. While creating qualification tests for each task type is considered as a common and reasonable way, this study investigates into its worker-filtering performance when the same qualification test is used across multiple types of tasks. On Amazon Mechanical Turk, we tested the annotation accuracy in six different cases where tasks consisted of two different difficulty levels, arising from the identical real-world domain: four combinatory cases in which the qualification test and the actual task were the same or different from each other, as well as two other cases where workers with Masters Qualification were asked to perform the actual task only. The experimental results demonstrated the two following findings: i) Workers that were assigned to a difficult qualification test scored better annotation accuracy regardless of the difficulty of the actual task; 
ii) Workers with Masters Qualification scored better annotation accuracy on the low-difficulty task but were not as accurate as those who passed a qualification test on the high-difficulty task.

Exploring Effectiveness of Inter-Microtask Qualification Tests in Crowdsourcing

While crowdsourcing provides a convenient solution for tapping into human intelligence, a concern is the bias inherent in the data collected. Events related to the COVID-19 pandemic had an impact on people globally, and crowdworkers were no exception. Given the evidence concerning mood and stress on work, we explore how temporal events might affect crowdsourced data. We replicated an image annotation task conducted in 2018, in which workers describe people images. We expected 2020 annotations to contain more references to health, as compared to 2018 data. Overall, we find no evidence that health-related tags were used more often in 2020, but instead we find a significant increase in the use of tags related to weight (e.g., fat, chubby, overweight). This result, coupled with the “stay at home” act in effect in 2020, illustrate how crowdwork is impacted by temporal events.

Crowdwork as a Snapshot in Time: Image Annotation Tasks during a Pandemic

We propose a crowdsourced approach for assessing political bias among news publishers. We aggregate pairwise comparisons between publishers into a relative ranking of leanings, in contrast to prior crowdsourcing approaches that request in-dependent ratings of publishers. We verify our approach by a preliminary study with 45 participants, who were asked to rank the political bias of 8 news publishers and to provide qualitative feedback on the study. We report our experimental results and describe our envisioned application scenario.

Assessing Political Bias using Crowdsourced Pairwise Comparisons

How to obtain higher quality task results with a fixed budget (labor cost) is one of the most important problems in practical crowdsourcing. We approach this problem by (1) dynamically choosing appropriate sets of tasks and workers during the worker-task assignment process and (2) introducing an ML worker that performs tasks as if it was a human worker. Our approach, named Dynamic Worker and Task Assignment with ML workers (DWTAML), chooses tasks and workers during the worker-task assignment process, based on the estimation of the reliability of workers and the confidence of task results at each moment. It extends the Dawid&Skene model and IEThresh. The results of an extensive set of experiments showed that DWTAML is effective in most cases.

Dynamic Worker-Task Assignment for High-Quality Task Results with ML Workers

Crowdsourcing offers a new form of work that makes it easy for people to work from home on their own computers. However, for some people with disabilities, such as visual impairment, problems are encountered in completing tasks. In this study, a questionnaire was used to investigate the working conditions of and problems encountered by visually impaired people on Japanese and US crowdsourcing platforms. The problems overcome by visually impaired people, difference between the visually impaired crowd workers from different countries, and possible solutions are provided herein. This research can be extended to more crowdsourcing platforms and languages in the future.

A Survey of Visually Impaired Workers in Japanese and US Crowdsourcing Platforms

In crowdsourcing, although there is a plenty of benefits in terms of cost and time to solve a complex task, there remain many challenges to reach into a consensus judgment from multiple crowd opinions. In this paper, we pay attention to a recently introduced crowd judgment model, termed as ``Constrained Crowd Judgment Analysis'' model. Unlike the traditional crowdsourced opinions (i.e., binary/multiple opinions), the option set in constrained judgment are undefined and there exist multiple components for a particular question. Moreover, there exist some constraints that should be preserved while deriving the final judgment, hence the problem becomes more difficult. Additionally, it is very difficult to rank the crowd workers as there is no ground truth present in this constrained judgment setting. In this paper, we introduce a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based multi-objective model for reaching consensus judgment from multiple crowd opinions as well as providing a better ranking among the crowd worker. The preliminary experimental study over a crowdsourced dataset demonstrates the effectiveness of the proposed model.

A TOPSIS-based Multi-objective Model for Constrained Crowd Judgment Analysis

Folk theories represent the users' beliefs of how an AI-driven system operate. Prior research investigated folk theories as a lens on users' reasoning about the algorithmically-curated content of news feeds on social media platforms primarily in interview studies and surveys. In this work-in-progress, we present an interactive interface for the structured capturing and crowdsourced collection of folk theories at scale.

Socially Augmented Crowdsourced Collection of Folk Theories

Extensive work has argued in favour of paying crowd workers a wage that is at least equivalent to the U.S. federal minimum wage. Meanwhile, research on collecting high quality annotations (e.g. for Natural Language Processing) suggests using qualifications such as a minimum number of previously completed tasks. If most requesters who pay fairly use this kind of minimum qualification, then workers may be forced to complete a substantial amount of poorly paid work for other requesters before they can earn a fair wage. This paper (1) explores current conventions for the threshold, (2) discusses possible alternatives, and (3) presents a study of correlation between approved work and work quality.

A Case for Soft Loss Functions

Next from HCOMP 2020

Verifying Extended Entity Relationship Diagrams with Open Tasks

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