Jorge Ramírez

Jorge Ramírez, Auday Berro, Marcos Baez et al.

A prominent approach to build datasets for training task-oriented bots is crowd-based paraphrasing. Current approaches, however, assume the crowd would naturally provide diverse paraphrases or focus only on lexical diversity. In this WiP we addressed an overlooked aspect of diversity, introducing an approach for guiding the crowdsourcing process towards paraphrases that are syntactically diverse.

Jorge Ramírez, Burcu Sayin, Marcos Baez et al.

Crowdsourcing is being increasingly adopted as a platform to run studies with human subjects. Running a crowdsourcing experiment involves several choices and strategies to successfully port an experimental design into an otherwise uncontrolled research environment, e.g., sampling crowd workers, mapping experimental conditions to micro-tasks, or ensure quality contributions. While several guidelines inform researchers in these choices, guidance of how and what to report from crowdsourcing experiments has been largely overlooked. If under-reported, implementation choices constitute variability sources that can affect the experiment's reproducibility and prevent a fair assessment of research outcomes. In this paper, we examine the current state of reporting of crowdsourcing experiments and offer guidance to address associated reporting issues. We start by identifying sensible implementation choices, relying on existing literature and interviews with experts, to then extensively analyze the reporting of 171 crowdsourcing experiments. Informed by this process, we propose a checklist for reporting crowdsourcing experiments.

Jorge Ramírez, Marcos Baez, Fabio Casati et al.

This paper explores and offers guidance on a specific and relevant problem in task design for crowdsourcing: how to formulate a complex question used to classify a set of items. In micro-task markets, classification is still among the most popular tasks. We situate our work in the context of information retrieval and multi-predicate classification, i.e., classifying a set of items based on a set of conditions. Our experiments cover a wide range of tasks and domains, and also consider crowd workers alone and in tandem with machine learning classifiers. We provide empirical evidence into how the resulting classification performance is affected by different predicate formulation strategies, emphasizing the importance of predicate formulation as a task design dimension in crowdsourcing.

Jorge Ramírez, Marcos Baez, Fabio Casati et al.

This paper reports on the challenges and lessons we learned while running controlled experiments in crowdsourcing platforms. Crowdsourcing is becoming an attractive technique to engage a diverse and large pool of subjects in experimental research, allowing researchers to achieve levels of scale and completion times that would otherwise not be feasible in lab settings. However, the scale and flexibility comes at the cost of multiple and sometimes unknown sources of bias and confounding factors that arise from technical limitations of crowdsourcing platforms and from the challenges of running controlled experiments in the "wild". In this paper, we take our experience in running systematic evaluations of task design as a motivating example to explore, describe, and quantify the potential impact of running uncontrolled crowdsourcing experiments and derive possible coping strategies. Among the challenges identified, we can mention sampling bias, controlling the assignment of subjects to experimental conditions, learning effects, and reliability of crowdsourcing results. According to our empirical studies, the impact of potential biases and confounding factors can amount to a 38\% loss in the utility of the data collected in uncontrolled settings; and it can significantly change the outcome of experiments. These issues ultimately inspired us to implement CrowdHub, a system that sits on top of major crowdsourcing platforms and allows researchers and practitioners to run controlled crowdsourcing projects.

Jorge Ramírez, Simone Degiacomi, Davide Zanella et al.

We present CrowdHub, a tool for running systematic evaluations of task designs on top of crowdsourcing platforms. The goal is to support the evaluation process, avoiding potential experimental biases that, according to our empirical studies, can amount to 38% loss in the utility of the collected dataset in uncontrolled settings. Using CrowdHub, researchers can map their experimental design and automate the complex process of managing task execution over time while controlling for returning workers and crowd demographics, thus reducing bias, increasing utility of collected data, and making more efficient use of a limited pool of subjects.

Jorge Ramírez, Marcos Baez, Fabio Casati et al.

Text classification is one of the most common goals of machine learning (ML) projects, and also one of the most frequent human intelligence tasks in crowdsourcing platforms. ML has mixed success in such tasks depending on the nature of the problem, while crowd-based classification has proven to be surprisingly effective, but can be expensive. Recently, hybrid text classification algorithms, combining human computation and machine learning, have been proposed to improve accuracy and reduce costs. One way to do so is to have ML highlight or emphasize portions of text that it believes to be more relevant to the decision. Humans can then rely only on this text or read the entire text if the highlighted information is insufficient. In this paper, we investigate if and under what conditions highlighting selected parts of the text can (or cannot) improve classification cost and/or accuracy, and in general how it affects the process and outcome of the human intelligence tasks. We study this through a series of crowdsourcing experiments running over different datasets and with task designs imposing different cognitive demands. Our findings suggest that highlighting is effective in reducing classification effort but does not improve accuracy - and in fact, low-quality highlighting can decrease it.