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Change of Perspective.

Photo: Paderborn University

Maximilian Spliethöver

Contact
Publications
 Maximilian Spliethöver

Computational Social Science

Research Associate

Phone:
+49 5251 60-6846
Office:
ZM2.A.03.04
Visitor:
Zukunftsmeile 2
33102 Paderborn

Open list in Research Information System

2021

Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

M. Alshomary, T. Gurcke, S. Syed, P. Heinisch, M. Spliethöver, P. Cimiano, M. Potthast, H. Wachsmuth, in: Proceedings of the 8th Workshop on Argument Mining, 2021


Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for Word Embedding Models

M. Spliethöver, H. Wachsmuth, in: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, 2021, pp. 552-559

Word embedding models reflect bias towards genders, ethnicities, and other social groups present in the underlying training data. Metrics such as ECT, RNSB, and WEAT quantify bias in these models based on predefined word lists representing social groups and bias-conveying concepts. How suitable these lists actually are to reveal bias - let alone the bias metrics in general - remains unclear, though. In this paper, we study how to assess the quality of bias metrics for word embedding models. In particular, we present a generic method, Bias Silhouette Analysis (BSA), that quantifies the accuracy and robustness of such a metric and of the word lists used. Given a biased and an unbiased reference embedding model, BSA applies the metric systematically for several subsets of the lists to the models. The variance and rate of convergence of the bias values of each model then entail the robustness of the word lists, whereas the distance between the models' values gives indications of the general accuracy of the metric with the word lists. We demonstrate the behavior of BSA on two standard embedding models for the three mentioned metrics with several word lists from existing research.


2020

Playful User-Generated Treatment: A Novel Game Design Approach for VR Exposure Therapy

D. Alexandrovsky, G. Volkmar, M. Spliethöver, S. Finke, M. Herrlich, T. Döring, J.D. Smeddinck, R. Malaka, in: Proceedings of the Annual Symposium on Computer-Human Interaction in Play, Association for Computing Machinery, 2020, pp. 32–45

Overcoming a range of challenges that traditional therapy faces, VRET yields great potential for the treatment of phobias such as acrophobia, the fear of heights. We investigate this potential and present playful user-generated treatment (PUT), a novel game-based approach for VRET. Based on a requirement analysis consisting of a literature review and semi-structured interviews with professional therapists, we designed and implemented the PUT concept as a two-step VR game design. To validate our approach, we conducted two studies. (1) In a study with 31 non-acrophobic subjects, we investigated the effect of content creation on player experience, motivation and height perception, and (2) in an online survey, we collected feedback from professional therapists. Both studies reveal that the PUT approach is well applicable. In particular, the analysis of the user study shows that the design phase leads to increased interest and enjoyment without notably influencing affective measures during the exposure session. Our work can help guiding researchers and practitioners at the intersection of game design and exposure therapy.


    Argument from Old Man's View: Assessing Social Bias in Argumentation

    M. Spliethöver, H. Wachsmuth, in: Proceedings of the 7th Workshop on Argument Mining (ArgMining 2020), 2020, pp. 76-87


    2019

    CLEF ProtestNews Lab 2019: Contextualized Word Embeddings for Event Sentence Detection and Event Extraction

    G. Skitalinskaya, J. Klaff, M. Spliethöver, 2019

    In this work we describe our results achieved in the ProtestNews Lab at CLEF 2019. To tackle the problems of event sentence detection and event extraction we decided to use contextualized string embeddings. The models were trained on a data corpus collected from Indian news sources, but evaluated on data obtained from news sources from other countries as well, such as China. Our models have obtained competitive results and have scored 3rd in the event sentence detection task and 1st in the event extraction task based on average F1-scores for different test datasets.


    Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit Segmentation

    M. Spliethöver, J. Klaff, H. Heuer, in: Proceedings of the 6th Workshop on Argument Mining, Association for Computational Linguistics, 2019, pp. 74-82

    Attention mechanisms have seen some success for natural language processing downstream tasks in recent years and generated new state-of-the-art results. A thorough evaluation of the attention mechanism for the task of Argumentation Mining is missing. With this paper, we report a comparative evaluation of attention layers in combination with a bidirectional long short-term memory network, which is the current state-of-the-art approach for the unit segmentation task. We also compare sentence-level contextualized word embeddings to pre-generated ones. Our findings suggest that for this task, the additional attention layer does not improve the performance. In most cases, contextualized embeddings do also not show an improvement on the score achieved by pre-defined embeddings.


    2018

    If You Ask Nicely: A Digital Assistant Rebuking Impolite Voice Commands

    M. Bonfert, M. Spliethöver, R. Arzaroli, M. Lange, M. Hanci, R. Porzel, in: Proceedings of the 20th ACM International Conference on Multimodal Interaction, 2018


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