We introduce an extensive dataset for multilingual probing of morphological information in language models (247 tasks across 42 languages from 10 families), each consisting of a sentence with a target word and a morphological tag as the desired label, derived from the Universal Dependencies treebanks. We find that pre-trained Transformer models (mBERT and XLM-RoBERTa) learn features that attain strong performance across these tasks. We then apply two methods to locate, for each probing task, where the disambiguating information resides in the input. The first is a new perturbation method that “masks” various parts of context; the second is the classical method of Shapley values. The most intriguing finding that emerges is a strong tendency for the preceding context to hold more information relevant to the prediction than the following context.

Previous research on design thinking (DT) has focused mainly on describing and explaining observed phenomena rather than manipulating variables within the boundaries of its principles to assess the impact of these changes. DT, as a human-centric problem-solving and innovation method, has varying baseline exposure levels of nondesigners and novices when codesigning. This qualitative research shows how DT can be redesigned to accommodate the inclusion and engagement of novices and nondesigners in the codesign process with enhanced DT method applicability. DT novices and nondesigners are challenged in codesign engagements with others and need an intuitive method that leads them to the same or better design results as a classic DT method. A redesign towards an intuitive DT method for nondesigners puts special consideration on method language, efficiency and cycle time. A case study validates its qualification under real-life circumstances when codesigning with DT novices and nondesigners. Overall, the research demonstrates that DT with regard to nondesigners can be simplified and redesigned for efficiency and effectiveness.

Argumentation problems are concerned with determining the acceptability of a set of arguments from their relational structure. When the available information is uncertain, probabilistic argumentation frameworks provide modeling tools to account for it. The first contribution of this paper is a novel interpretation of probabilistic argumentation frameworks as probabilistic logic programs. Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. We show that the programs representing probabilistic argumentation frameworks do not satisfy a common assumption in probabilistic logic programming (PLP) semantics, which is, that probabilistic facts fully capture the uncertainty in the domain under investigation. The second contribution of this paper is then a novel PLP semantics for programs where a choice of probabilistic facts does not uniquely determine the truth assignment of the logical atoms. The third contribution of this paper is the implementation of a PLP system supporting this semantics: smProbLog. smProbLog is a novel PLP framework based on the PLP language ProbLog. smProbLog supports many inference and learning tasks typical of PLP, which, together with our first contribution, provide novel reasoning tools for probabilistic argumentation. We evaluate our approach with experiments analyzing the computational cost of the proposed algorithms and their application to a dataset of argumentation problems.