Even though hate speech (HS) online has been an important object of research in the last decade, most HS-related corpora over-simplify the phenomenon of hate by attempting to label user comments as “hate” or “neutral”. This ignores the complex and subjective nature of HS, which limits the real-life applicability of classifiers trained on these corpora. In this study, we present the M-Phasis corpus, a corpus of ~9k German and French user comments collected from migration-related news articles. It goes beyond the “hate”-“neutral” dichotomy and is instead annotated with 23 features, which in combination become descriptors of various types of speech, ranging from critical comments to implicit and explicit expressions of hate. The annotations are performed by 4 native speakers per language and achieve high (0.77 <= k <= 1) inter-annotator agreements. Besides describing the corpus creation and presenting insights from a content, error and domain analysis, we explore its data characteristics by training several classification baselines.

Documents as short as a single sentence may inadvertently reveal sensitive information about their authors, including e.g.their gender or ethnicity.Style transfer is an effective way of transforming texts in order to remove any information that enables author profiling.However, for a number of current state-of-theart approaches the improved privacy is accompanied by an undesirable drop in the downstream utility of the transformed data.In this paper, we propose a simple, zero-shot way to effectively lower the risk of author profiling through multilingual back-translation using off-the-shelf translation models.We compare our models with five representative text style transfer models on three datasets across different domains.Results from both an automatic and a human evaluation show that our approach achieves the best overall performance while requiring no training data.We are able to lower the adversarial prediction of gender and race by up to 22% while retaining 95% of the original utility on downstream tasks.

Machine Learning approaches to Natural Language Processing tasks benefit from a comprehensive collection of real-life user data. At the same time, there is a clear need for protecting the privacy of the users whose data is collected and processed. For text collections, such as, e.g., transcripts of voice interactions or patient records, replacing sensitive parts with benign alternatives can provide de-identification. However, how much privacy is actually guaranteed by such text transformations, and are the resulting texts still useful for machine learning? In this paper, we derive formal privacy guarantees for general text transformation-based de-identification methods on the basis of Differential Privacy. We also measure the effect that different ways of masking private information in dialog transcripts have on a subsequent machine learning task. To this end, we formulate different masking strategies and compare their privacy-utility trade-offs. In particular, we compare a simple redact approach with more sophisticated word-by-word replacement using deep learning models on multiple natural language understanding tasks like named entity recognition, intent detection, and dialog act classification. We find that only word-by-word replacement is robust against performance drops in various tasks.

We present a group recommender system for vacations that helps group members who are not able to communicate synchronously to specify their preferences collaboratively and to arrive at an agreement about an overall solution. The system's design includes two innovations in visual user interfaces: 1. An interface for collaborative preference specification offers various ways in which one group member can view and perhaps copy the previously specified preferences of other users. This interface has been found to further mutual understanding and agreement. The same interface is used by the system to display recommended solutions and to visualize the extent to which a solution satisfies the preferences of the various group members. 2. In a novel application of animated characters, each character serves as a representative of a group member who is not currently available for communication. By responding with speech, facial expressions, and gesture to proposed solutions, a representative conveys to the current real user some key aspects of the corresponding real group member's responses to a proposed solution. Taken together, these two aspects of the interface provide complementary and partly redundant means by which a group member can achieve awareness of the preferences and responses of other group members: an abstract, complete, graphical representation and a concrete, selective, human-like representation. By allowing users to choose flexibly which representation they will attend to under what circumstances, we aim to move beyond the usual debates about the relative merits of these two general types of representation.