Computational approaches for engineering effective teams

The performance of a team depends not only on the abilities of its individual members, but also on how these members interact with each other. Inspired by this premise and motivated by a large number of applications in educational, industrial and management settings, this thesis studies a family of problems, known as team-formation problems, that aim to engineer teams that are effective and successful. The major challenge in this family of problems is dealing with the complexity of the human team participants. Specifically, each individual has his own objectives, demands, and constraints that might be in contrast with the desired team objective. Furthermore, different collaboration models lead to different instances of team-formation problems. In this thesis, we introduce several such models and describe techniques and efficient algorithms for various instantiations of the team-formation problem. This thesis consists of two main parts. In the first part, we examine three distinct team-formation problems that are of significant interest in (i) educational settings, (ii) industrial organizations, and (iii) management settings respectively. What constitutes an effective team in each of the aforementioned settings is totally dependent on the objective of the team. For instance, the performance of a team (or a study group) in an educational setting can be measured as the amount of learning and collaboration that takes place inside the team. In industrial organizations, desirable teams are those that are cost-effective and highly profitable. Finally in management settings, an interesting body of research uncovers that teams with faultlines are prone to performance decrements. Thus, the challenge is to form teams that are free of faultlines, that is, to form teams that are robust and less likely to break due to disagreements. The first part of the thesis discusses approaches for formalizing these problems and presents efficient computational methods for solving them. In the second part of the thesis, we consider the problem of improving the functioning of existing teams. More precisely, we show how we can use models from social theory to capture the dynamics of the interactions between the team members. We further discuss how teams can be modified so that the interaction dynamics lead to desirable outcomes such as higher levels of agreement or lesser tension and conflict among the team members

A Team-Formation Algorithm for Faultline Minimization

In recent years, the proliferation of online resumes and the need to evaluate large populations of candidates for on-site and virtual teams have led to a growing interest in automated team-formation. Given a large pool of candidates, the general problem requires the selection of a team of experts to complete a given task. Surprisingly, while ongoing research has studied numerous variations with different constraints, it has overlooked a factor with a well-documented impact on team cohesion and performance: team faultlines. Addressing this gap is challenging, as the available measures for faultlines in existing teams cannot be efficiently applied to faultline optimization. In this work, we meet this challenge with a new measure that can be efficiently used for both faultline measurement and minimization. We then use the measure to solve the problem of automatically partitioning a large population into low-faultline teams. By introducing faultlines to the team-formation literature, our work creates exciting opportunities for algorithmic work on faultline optimization, as well as on work that combines and studies the connection of faultlines with other influential team characteristics

Emu: Enhancing Multilingual Sentence Embeddings with Semantic Specialization

We present Emu, a system that semantically enhances multilingual sentence embeddings. Our framework fine-tunes pre-trained multilingual sentence embeddings using two main components: a semantic classifier and a language discriminator. The semantic classifier improves the semantic similarity of related sentences, whereas the language discriminator enhances the multilinguality of the embeddings via multilingual adversarial training. Our experimental results based on several language pairs show that our specialized embeddings outperform the state-of-the-art multilingual sentence embedding model on the task of cross-lingual intent classification using only monolingual labeled data.Comment: AAAI 202

SubjQA: A Dataset for Subjectivity and Review Comprehension

Subjectivity is the expression of internal opinions or beliefs which cannot be objectively observed or verified, and has been shown to be important for sentiment analysis and word-sense disambiguation. Furthermore, subjectivity is an important aspect of user-generated data. In spite of this, subjectivity has not been investigated in contexts where such data is widespread, such as in question answering (QA). We therefore investigate the relationship between subjectivity and QA, while developing a new dataset. We compare and contrast with analyses from previous work, and verify that findings regarding subjectivity still hold when using recently developed NLP architectures. We find that subjectivity is also an important feature in the case of QA, albeit with more intricate interactions between subjectivity and QA performance. For instance, a subjective question may or may not be associated with a subjective answer. We release an English QA dataset (SubjQA) based on customer reviews, containing subjectivity annotations for questions and answer spans across 6 distinct domains.Comment: EMNLP 2020 Long Paper - Camera Read

Finding low-tension communities

International audienceMotivated by applications that arise in online social media and collaboration networks, there has been a lot of work on community-search. In this class of problems, the goal is to find a subgraph that satisfies a certain connectivity requirement and contains a given collection of seed nodes.In this paper, we extend the community-search problem by associating each individual with a profile. The profile is a numeric score that quantifies the position of an individual with respect to a topic. We adopt a model where each individual starts with a latent profile and arrives to a conformed profile through a dynamic conformation process, which takes into account the individual's social interaction and the tendency to conform with one's social environment. In this framework, social tension arises from the differences between the conformed profiles of neighboring individuals as well as from the differences between individuals' conformed and latent profiles.Given a network of individuals, their latent profiles and this conformation process, we extend the community-search problem by requiring the output subgraphs to have low social tension. From the technical point of view, we study the complexity of this problem and propose algorithms for solving it effectively. Our experimental evaluation in a number of social networks reveals the efficacy and efficiency of our methods