Linking Programming to Representations : Understanding meaning-making in physics education through semiotic resources

Programming is investigated with a social semiotic lens to study how programming can contribute to meaning-making in physics. The analysis focuses on how new and dynamic representations can be created with the help of programming and how programming allows students to investigate and create their own models of physical phenomena. This is described from a semiotic perspective and the semiotic perspective is also developed with new theoretical constructions to better describe the students' use of representations in physics

Strong Generative Capacity of Morphological Processes

Morphological processes are generally computable with 1-way finite-state transducers. However, we show that 1-way transducers do not capture the strong generative capacity of certain morphological analyses for more complex processes, including mobile affixation, infixation, and partial reduplication. As diagnostics for strong generative capacity, we use origin semantics and order-preservation. These analyze the input-output correspondences generated by finite-state transducers and their corresponding logical transductions. For some linguistic analyses of these complex processes, their strong generative capacity is matched by more expressive grammars, such as non-order-preserving transductions and their corresponding 2-way finite-state transducers

Local String Transduction as Sequence Labeling

[EN]We show that the general problem of string transduction can be reduced to the problem of sequence labeling. While character deletion and insertions are allowed in string transduction, they do not exist in sequence labeling. We show how to overcome this difference. Our approach can be used with any sequence labeling algorithm and it works best for problems in which string transduction imposes a strong notion of locality (no long range dependencies). We experiment with spelling correction for social media, OCR correction, and morphological inflection, and we see that it behaves better than seq2seq models and yields state-of-the-art results in several cases.Peer reviewe

A Praxical Solution of the Symbol Grounding Problem

Peer reviewe

From art to artefact: meaning-making processes across the three major subjects in a Diploma in Fashion

Includes abstract.Includes bibliographical references.Investigations in the field of Fashion design education have not taken into account that students need to negotiate three very different subjects. In particular the technical side, namely pattern making and garment construction have not received enough attention. Over the years I have found the same difficulties among students as they negotiate the three main subjects. Their encounter with the technical subjects, presents particular difficulties. In order to explore these difficulties, this study investigates the meaning-making processes of beginner students as they move from drawing and designing to production of a garment. By identifying and analysing the practices of a beginner, I examine how students become multimodally literate across the three subjects

Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop

The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques specifically developed for analyzing and understanding the inner-workings and representations acquired by neural models of language. Approaches included: systematic manipulation of input to neural networks and investigating the impact on their performance, testing whether interpretable knowledge can be decoded from intermediate representations acquired by neural networks, proposing modifications to neural network architectures to make their knowledge state or generated output more explainable, and examining the performance of networks on simplified or formal languages. Here we review a number of representative studies in each category