Maike Paetzel

Jana Götze, Maike Paetzel-Prüsmann, Wencke Liermann et al.

This paper presents the slurk software, a lightweight interaction server for setting up dialog data collections and running experiments. Slurk enables a multitude of settings including text-based, speech and video interaction between two or more humans or humans and bots, and a multimodal display area for presenting shared or private interactive context. The software is implemented in Python with an HTML and JS frontend that can easily be adapted to individual needs. It also provides a setup for pairing participants on common crowdworking platforms such as Amazon Mechanical Turk and some example bot scripts for common interaction scenarios.

Giulia Perugia, Maike Paetzel-Prüssman, Isabelle Hupont et al.

In this paper, we present a study aimed at understanding whether the embodiment and humanlikeness of an artificial agent can affect people's spontaneous and instructed mimicry of its facial expressions. The study followed a mixed experimental design and revolved around an emotion recognition task. Participants were randomly assigned to one level of humanlikeness (between-subject variable: humanlike, characterlike, or morph facial texture of the artificial agents) and observed the facial expressions displayed by a human (control) and three artificial agents differing in embodiment (within-subject variable: video-recorded robot, physical robot, and virtual agent). To study both spontaneous and instructed facial mimicry, we divided the experimental sessions into two phases. In the first phase, we asked participants to observe and recognize the emotions displayed by the agents. In the second phase, we asked them to look at the agents' facial expressions, replicate their dynamics as closely as possible, and then identify the observed emotions. In both cases, we assessed participants' facial expressions with an automated Action Unit (AU) intensity detector. Contrary to our hypotheses, our results disclose that the agent that was perceived as the least uncanny, and most anthropomorphic, likable, and co-present, was the one spontaneously mimicked the least. Moreover, they show that instructed facial mimicry negatively predicts spontaneous facial mimicry. Further exploratory analyses revealed that spontaneous facial mimicry appeared when participants were less certain of the emotion they recognized. Hence, we postulate that an emotion recognition goal can flip the social value of facial mimicry as it transforms a likable artificial agent into a distractor.

Jakob Nyberg, Ramesh Manuvinakurike, Maike Paetzel-Prüsmann

Human ratings are one of the most prevalent methods to evaluate the performance of natural language processing algorithms. Similarly, it is common to measure the quality of sentences generated by a natural language generation model using human raters. In this paper, we argue for exploring the use of subjective evaluations within the process of training language generation models in a multi-task learning setting. As a case study, we use a crowd-authored dialogue corpus to fine-tune six different language generation models. Two of these models incorporate multi-task learning and use subjective ratings of lines as part of an explicit learning goal. A human evaluation of the generated dialogue lines reveals that utterances generated by the multi-tasking models were subjectively rated as the most typical, most moving the conversation forward, and least offensive. Based on these promising first results, we discuss future research directions for incorporating subjective human evaluations into language model training and to hence keep the human user in the loop during the development process.

Maike Paetzel, Ramesh Manuvinakurike

In an increasingly globalized world, geographic literacy is crucial. In this paper, we present a collaborative two-player game to improve people's ability to locate countries on the world map. We discuss two implementations of the game: First, we created a web-based version which can be played with the remote-controlled agent Nellie. With the knowledge we gained from a large online data collection, we re-implemented the game so it can be played face-to-face with the Furhat robot Neil. Our analysis shows that participants found the game not just engaging to play, they also believe they gained lasting knowledge about the world map.