Automated scoring of student essays is increasingly used to reduce manual grading effort. State-of-the-art approaches use supervised machine learning which makes it complicated to transfer a system trained on one task to another. We investigate which currently used features are task-independent and evaluate their transferability on English and German datasets. We find that, by using our task-independent feature set, models transfer better between tasks. We also find that the transfer works even better between tasks of the same type.

In this paper, we describe our participation in the first shared task on automated stance detection (SemEval 2016 Task 6). We consider the task as a multidimensional classification problem and thus use a sequence of stacked classifiers. For subtask A, we utilize a rich feature set that does not rely on external information such as additional tweets or knowledge bases. For subtask B, we rely on the similarity of tweets in this task with tweets from subtask A in order to transfer the models learnt in subtask A.

Abstract. We analyze whether implicitness affects human perception of hate speech. To do so, we use Tweets from an existing hate speech corpus and paraphrase them with rules to make the hate speech they contain more explicit. Comparing the judgment on the original and the paraphrased Tweets, our study indicates that implicitness is a factor in human and automatic hate speech detection. Hence, our study suggests that current automatic hate speech detection needs features that are more sensitive to implicitness.

The UN Convention on the Rights of Persons with Disabilities Article 24 states that "States Parties shall ensure inclusive education at all levels of education and life long learning." This article focuses on the inclusion of people with visual impairments in learning processes including complex table-based data. Gaining insight into and understanding of complex data is a highly demanding task for people with visual impairments. Especially in the case of table-based data, the classic approaches of braille-based output devices and printing concepts are limited. Haptic perception requires sequential information processing rather than the parallel processing used by the visual system, which hinders haptic perception to gather a fast overview of and deeper insight into the data. Nevertheless, neuroscientific research has identified great dependencies between haptic perception and the cognitive processing of visual sensing. Based on these findings, we developed a haptic 3D surface representation of classic diagrams and charts, such as bar graphs and pie charts. In a qualitative evaluation study, we identified certain advantages of our relief-type 3D chart approach. Finally, we present an education model for German schools that includes a 3D printing approach to help integrate students with visual impairments.

This paper describes LTL-UDE's systems for the SemEval 2019 Shared Task 6. We present results for Subtask A and C. In Subtask A, we experiment with an embedding representation of postings and use a Multi-Layer Perceptron and BERT to categorize postings. Our best result reaches the 10th place (out of 103) using BERT. In Subtask C, we applied a two-vote classification approach with minority fallback, which is placed on the 19th rank (out of 65).