Sebis at SemEval-2023 Task 7: A Joint System for Natural Language Inference and Evidence Retrieval from Clinical Trial Reports

With the increasing number of clinical trial reports generated every day, it is becoming hard to keep up with novel discoveries that inform evidence-based healthcare recommendations. To help automate this process and assist medical experts, NLP solutions are being developed. This motivated the SemEval-2023 Task 7, where the goal was to develop an NLP system for two tasks: evidence retrieval and natural language inference from clinical trial data. In this paper, we describe our two developed systems. The first one is a pipeline system that models the two tasks separately, while the second one is a joint system that learns the two tasks simultaneously with a shared representation and a multi-task learning approach. The final system combines their outputs in an ensemble system. We formalize the models, present their characteristics and challenges, and provide an analysis of achieved results.Comment: 6 pages, SemEval 202

NLP for Consumer Protection: Battling Illegal Clauses in German Terms and Conditions in Online Shopping

Online shopping is an ever more important part of the global consumer economy, not just in times of a pandemic. When we place an order online as consumers, we regularly agree to the so-called “Terms and Conditions” (T&C), a contract unilaterally drafted by the seller. Often, consumers do not read these contracts and unwittingly agree to unfavourable and often void terms. Government and non-government organisations (NGOs) for consumer protection battle such terms on behalf of consumers, who often hesitate to take on legal actions themselves. However, the growing number of online shops and a lack of funding makes it increasingly difficult for such organisations to monitor the market effectively. This paper describes how Natural Language Processing (NLP) can be applied to support consumer advocates in their efforts to protect consumers. Together with two NGOs from Germany, we developed an NLP-based application that legally assesses clauses in T&C from German online shops under the European Union’s (EU) jurisdiction. We report that we could achieve an accuracy of 0.9 in the detection of void clauses by fine-tuning a pre-trained German BERT model. The approach is currently used by two NGOs and has already helped to challenge void clauses in T&C

Empowering End-users to Collaboratively Manage and Analyze Evolving Data Models

In order to empower end-users to make well-founded decisions based on domain-specific knowledge, companies use end-user oriented business intelligence (BI) software like spreadsheets. Moreover, many decisions require the collaboration of multiple and autonomous knowledge workers. However, prevalent BI software does not provide elevated collaboration features as known from traditional Web 2.0 technologies. There is also a lack of research on how to integrate collaboration features into BI systems, and which challenges arise as a consequence. In the paper at hand we address this issue by proposing the Spreadsheet 2.0 approach, which integrates Web 2.0 features with the spreadsheet paradigm as most-common representative of end-user-oriented business intelligence tools. Therefore, we derive requirements for a Web 2.0-based approach to collaborative BI, and present the conceptual design for a Spreadsheet 2.0 solution. Subsequently, we demonstrate a corresponding prototypical implementation, and elaborate on key findings and main challenges identified by its application and evaluation

A Pathway for the Practical Adoption of Federated Machine Learning Projects

Big data forms the fundamental basis for the success of Machine Learning. Yet, a large amount of the world’s digitized data is locked up in data silos, leaving its potential untapped. Federated Machine Learning is a novel Machine Learning paradigm with the potential to overcome data silos by enabling the decentralized training of Machine Learning models through a model-to-data approach. Despite its potential advantages, most Federated Machine Learning projects fail to actualize due to their decentralized structure and incomprehensive interrelations. Current literature lacks clear guidelines on which steps need to be performed to successfully implement Federated Machine Learning projects. This study aims to close this research gap. Through a design science research approach, we provide three distinct activity models which outline required tasks in the development of Federated Machine Learning systems. Thereby, we aim to reduce complexity and ease the implementation process by guiding practitioners through the project life cycle