Research into automatic text simplification aims to promote access to information for all members of society. To facilitate generalizability, simplification research often abstracts away from specific use cases, and targets a prototypical reader and an underspecified content creator. In this paper, we consider a real-world use case – simplification technology for use in Dutch municipalities – and identify the needs of the content creators and the target audiences in this scenario. The stakeholders envision a system that (a) assists the human writer without taking over the task; (b) provides diverse outputs, tailored for specific target audiences; and (c) explains the suggestions that it outputs. These requirements call for technology that is characterized by modularity, explainability, and variability. We argue that these are important research directions that require further exploration
MULTIFILE
In this paper we describe our work in progress on the development of a set of criteria to predict text difficulty in Sign Language of the Netherlands (NGT). These texts are used in a four year bachelor program, which is being brought in line with the Common European Framework of Reference for Languages (Council of Europe, 2001). Production and interaction proficiency are assessed through the NGT Functional Assessment instrument, adapted from the Sign Language Proficiency Interview (Caccamise & Samar, 2009). With this test we were able to determine that after one year of NGT-study students produce NGT at CEFR-level A2, after two years they sign at level B1, and after four years they are proficient in NGT on CEFR-level B2. As a result of that we were able to identify NGT texts that were matched to the level of students at certain stages in their studies with a CEFR-level. These texts were then analysed for sign familiarity, morpheme-sign rate, use of space and use of non-manual signals. All of these elements appear to be relevant for the determination of a good alignment between the difficulty of NGT signed texts and the targeted CEFR level, although only the morpheme-sign rate appears to be a decisive indicator
DOCUMENT
Robots are increasingly used in a variety of work environments, but surprisingly little attention has been paid to how robots change work. In this comparative case study, we explore how robotization changed the work design of order pickers and order packers in eight logistic warehouses. We found that all warehouses robotized tasks based on technological functionality to increase efficiency, which sometimes created jobs consisting of ‘left-over tasks’. Only two warehouses used a bottom-up approach, where employees were involved in the implementation and quality of work was considered important. Although the other warehouses did not, sometimes their work design still benefitted from robotization. The positive effects we identified are reduced physical and cognitive demands and opportunities for upskilling. Warehouses that lack attention to the quality of work may risk ending up with the negative effects for employees, such as simplification and intensification of work, and reduced autonomy. We propose that understanding the consequences of robots on work design supports HR professionals to help managing this transition by both giving relevant input on a strategic level about the importance of work design and advocating for employees and their involvement.
DOCUMENT
