While automatic term extraction is a wellresearched area, computational approaches to distinguish between degrees of technicality are still understudied.We semi-automatically create a German gold standard of technicality across four domains, and illustrate the impact of a web-crawled general-language corpus on predicting technicality.When defining a classification approach that combines general-language and domain-specific word embeddings, we go beyond previous work and align vector spaces to gain comparative embeddings.We suggest two novel models to exploit general-vs.domain-specific comparisons: a simple neural network model with pre-computed comparative-embedding information as input, and a multi-channel model computing the comparison internally.Both models outperform previous approaches, with the multi-channel model performing best.

Effectively supporting novices during performance of complex tasks, e.g. do-it-yourself (DIY) projects, requires intelligent assistants to be more than mere instructors. In order to be accepted as a competent and trustworthy cooperation partner, they need to be able to actively participate in the project and engage in helpful conversations with users when assistance is necessary. Therefore, a new proactive version of the DIY-assistant Robert is presented in this paper. It extends the previous prototype by including the capability to initiate reflective meta-dialogues using multimodal cues. Two different strategies for reflective dialogue are implemented: A progress-based strategy initiates a reflective dialogue about previous experience with the assistance for encouraging the self-appraisal of the user. An activity-based strategy is applied for providing timely, task-dependent support. Therefore, user activities with a connected drill driver are tracked that trigger dialogues in order to reflect on the current task and to prevent task failure. An experimental study comparing the proactive assistant against the baseline version shows that proactive meta-dialogue is able to build user trust significantly better than a solely reactive system. Besides, the results provide interesting insights for the development of proactive dialogue assistants.

We present a dataset with difficulty ratings for 1,030 German closed noun compounds extracted from domain-specific texts for do-it-ourself (DIY), cooking and automotive. The dataset includes two-part compounds for cooking and DIY, and two- to four-part compounds for automotive. The compounds were identified in text using the Simple Compound Splitter (Weller-Di Marco, 2017); a subset was filtered and balanced for frequency and productivity criteria as basis for manual annotation and fine-grained interpretation. This study presents the creation, the final dataset with ratings from 20 annotators and statistics over the dataset, to provide insight into the perception of domain-specific term difficulty. It is particularly striking that annotators agree on a coarse, binary distinction between easy vs. difficult domain-specific compounds but that a more fine grained distinction of difficulty is not meaningful. We finally discuss the challenges of an annotation for difficulty, which includes both the task description as well as the selection of the data basis.

Novice users require assistance when performing handicraft tasks. Adequate instruction ensures task completion and conveys knowledge and abilities required to perform the task. We present an assistant teaching novice users how to operate electronic tools, such as drills, saws, and sanders, in the context of Do-It-Yourself (DIY) home improvement projects. First, the actions that need to be performed for the project are determined by a planner. Second, a dialogue manager capable of natural language interaction presents these actions as instructions to the user. Third, questions on these actions and involved objects are answered by generating appropriate ontology-based explanations.