Rediscovering Hashed Random Projections for Efficient Quantization of Contextualized Sentence Embeddings

Training and inference on edge devices often requires an efficient setup due to computational limitations. While pre-computing data representations and caching them on a server can mitigate extensive edge device computation, this leads to two challenges. First, the amount of storage required on the server that scales linearly with the number of instances. Second, the bandwidth required to send extensively large amounts of data to an edge device. To reduce the memory footprint of pre-computed data representations, we propose a simple, yet effective approach that uses randomly initialized hyperplane projections. To further reduce their size by up to 98.96%, we quantize the resulting floating-point representations into binary vectors. Despite the greatly reduced size, we show that the embeddings remain effective for training models across various English and German sentence classification tasks that retain 94%--99% of their floating-point

Epistemic and social scripts in computer-supported collaborative learning

Collaborative learning in computer-supported learning environments typically means that learners work on tasks together, discussing their individual perspectives via text-based media or videoconferencing, and consequently acquire knowledge. Collaborative learning, however, is often sub-optimal with respect to how learners work on the concepts that are supposed to be learned and how learners interact with each other. One possibility to improve collaborative learning environments is to conceptualize epistemic scripts, which specify how learners work on a given task, and social scripts, which structure how learners interact with each other. In this contribution, two studies will be reported that investigated the effects of epistemic and social scripts in a text-based computer-supported learning environment and in a videoconferencing learning environment in order to foster the individual acquisition of knowledge. In each study the factors ‘epistemic script’ and ‘social script’ have been independently varied in a 2×2-factorial design. 182 university students of Educational Science participated in these two studies. Results of both studies show that social scripts can be substantially beneficial with respect to the individual acquisition of knowledge, whereas epistemic scripts apparently do not to lead to the expected effects

Event-Related Potentials Reveal Rapid Verification of Predicted Visual Input

Human information processing depends critically on continuous predictions about upcoming events, but the temporal convergence of expectancy-based top-down and input-driven bottom-up streams is poorly understood. We show that, during reading, event-related potentials differ between exposure to highly predictable and unpredictable words no later than 90 ms after visual input. This result suggests an extremely rapid comparison of expected and incoming visual information and gives an upper temporal bound for theories of top-down and bottom-up interactions in object recognition

Das Digitale Wörterbuch der Deutschen Sprache (DWDS)

No area in the study of human languages has a longer history and a higher practical signifi cance than lexicography. The advent of the computer has dramaticually changed this discipline in ways which go far beyond the digitisation of materials in combination with effi cient search tools, or the transfer of an existing dictionary onto the computer. They allow the stepwise elaboration of what is called here Digital Lexical Systems, i.e., computerized systems in which the underlying data - in form of an extendable corpus - and description of lexical properties on various levels can be effi ciently combined. This paper discusses the range of these possibilities and describes the present form of the German „Digital Lexical System of the Academy“, a project of the Berlin-Brandenburg Academy of Sciences (www.dwds.de)