Papers by Wolfgang Minker
Comparative Study of Sentence Embeddings for Contextual Paraphrasing (2020.lrec-1)
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| Challenge: | Paraphrasing is an important aspect of natural-language generation that can produce more variety in the way specific content is presented. |
| Approach: | They propose to use contextual paraphrasing to capture the meaning of a sentence while performing dialogue act clustering. |
| Outcome: | The proposed task combines paraphrases with dialogue act clustering to capture such contextual paraphrasing. |
Effects of Gender Stereotypes on Trust and Likability in Spoken Human-Robot Interaction (L18-1)
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| Challenge: | a study investigates the influence of gender stereotypes on trust and likability of humanoid robots . explicit gender and stereotypicality of a task are manipulated to influence robot behavior . future research may look into situational variables that drive stereotypification in robot interaction . |
| Approach: | They investigated the influence of gender stereotypes on trust and likability of robots . they used explicit (name and voice) and implicit (personality) genders to manipulate stereotypical tasks . future research may look into situational variables that drive stereotypization . |
| Outcome: | The findings suggest that gender stereotypes need to be differentiated in robot interaction . the gender and personality characteristics of robots influence trust and likability . |
Estimating User Communication Styles for Spoken Dialogue Systems (2020.lrec-1)
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| Challenge: | a neural network estimation system for spoken dialogues can be used to estimate the communication style of a user's interaction, but this is rarely implemented in a live system. |
| Approach: | They propose a neural network approach to estimate the communication style of spoken interaction, namely elaborateness and directness. |
| Outcome: | The proposed method can estimate the elaborateness and directness of spoken interaction and improve the results with additional linguistic features. |
Expert Evaluation of a Spoken Dialogue System in a Clinical Operating Room (L18-1)
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| Challenge: | With the emergence of new technologies, the surgical working environment becomes increasingly complex and comprises many medical devices which have to be monitored and controlled. |
| Approach: | They propose to use natural spoken language to control surgical operating rooms to reduce the amount of staff needed during a procedure. |
| Outcome: | The proposed system can control the operating room using natural spoken language and is evaluated by experts in the field of minimally invasive surgery. |
On the Vector Representation of Utterances in Dialogue Context (L18-1)
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| Challenge: | In recent years, the representation of words as vectors in a vector space has gained a high degree of attention in the research community. |
| Approach: | They introduce a new language resource that represents dialogue utterances in vector space and captures the semantic meaning of those utterrances in the dialogue context. |
| Outcome: | The proposed model captures relevant semantic information by comparing them to manually annotated dialogue acts. |
What Causes the Differences in Communication Styles? A Multicultural Study on Directness and Elaborateness (L18-1)
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| Challenge: | Using a multi-cultural approach, we investigated the differences in the communication styles elaborateness and directness of human-computer interaction. |
| Approach: | They propose to design a Spoken Dialogue System which adapts to the user's communication idiosyncrasies and to examine the influence of the user culture and gender on the system's elaborateness and directness. |
| Outcome: | The proposed system could be used to communicate with computers in a human-computer interaction. |
ProDial – An Annotated Proactive Dialogue Act Corpus for Conversational Assistants using Crowdsourcing (2022.lrec-1)
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| Challenge: | Especially in the household domain, robots may become indispensable helpers by overtaking tedious tasks, e.g. keeping the place tidy. |
| Approach: | They propose a conversational approach for explicitly collecting personal user information using natural dialogue. |
| Outcome: | The proposed approach is compared to a baseline dialogue strategy for interactive personalization and has shown that it is friendlier. |
A Comparison of Explicit and Implicit Proactive Dialogue Strategies for Conversational Recommendation (2020.lrec-1)
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| Challenge: | Existing literature on traditional and conversational recommendation systems, but how to provide suggestions is still an open question. |
| Approach: | They propose to use explicit and implicit strategies to compare user preferences and a proactive strategy to provide information from a gathered system to determine user acceptance. |
| Outcome: | The proposed strategies significantly influence the perception of human-computer interaction. |
User Interest Modelling in Argumentative Dialogue Systems (2022.lrec-1)
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| Challenge: | Existing studies on user interest in dialogue systems depend on explicit user feedback. |
| Approach: | They propose a model to implicitly estimate user interest during argumentative dialogues based on semantically clustered data. |
| Outcome: | The proposed model achieves a classification accuracy of 74.9% and tested with different Artificial Neural Networks (ANN) which new argument would fit the user interest best. |
Evaluation of Argument Search Approaches in the Context of Argumentative Dialogue Systems (2020.lrec-1)
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| Challenge: | Argumentative dialogue systems and chat bots require a database of arguments that matches their requirements. |
| Approach: | They propose a dialogue system that presents arguments by virtual avatar and synthetic speech to users and allows them to rate the presented content in four different categories. |
| Outcome: | The proposed system evaluates arguments retrieved by two state-of-the-art argument search engines and a system based on traditional web search. |
Towards Modelling Self-imposed Filter Bubbles in Argumentative Dialogue Systems (2022.lrec-1)
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| Challenge: | In order to overcome this “self-imposed filter bubble” (SFB), it is crucial to identify influential indicators for the user’s SFB, namely Reflective User Engagement (RUE), Personal Relevance ranking of content-related subtopics as well as False (FK) and True Knowledge (TK). |
| Approach: | They propose to model an SFB by focusing on four indicators for the user's Reflective User Engagement (RUE), their Personal Relevance ranking of content-related subtopics and their False (FK) and True Knowledge (TK) indicators are based on the responses of 202 users of an online argumentative dialogue system BEA. |
| Outcome: | The proposed system aims to break the self-imposed filter bubble (SFB) by identifying indicators for the user's SFB . |
Towards Speech-only Opinion-level Sentiment Analysis (2022.lrec-1)
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| Challenge: | Existing systems that estimate user preferences only in static manners or exploit interaction history are inadequate to accurately assess user preferences. |
| Approach: | They propose to integrate rank consistent ordinal regression into a speech-only sentiment prediction task performed by ResNet-like systems and use speaker verification extractors trained on larger datasets as low-level feature extractor. |
| Outcome: | The proposed system beats state-of-the-art unimodal systems on multimodal Opinion Sentiment and Emotion Intensity databases. |
Towards Building a Spoken Dialogue System for Argument Exploration (2022.lrec-1)
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| Challenge: | Argumentative dialogue systems lack a robust natural language understanding framework for complex tasks . drop-down menus hinder the application of natural language learning approaches . |
| Approach: | They propose to integrate a natural language understanding framework into an argumentative dialogue system. |
| Outcome: | The proposed system is compared to a baseline system using a drop-down menu . the drop- down menu convinces, but the willingness to use it is significantly higher . |
Contextual Dependencies in Time-Continuous Multidimensional Affect Recognition (L18-1)
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| Challenge: | despite of the research done in this area there is still no agreement on this issue. |
| Approach: | a paper compares the amount of context used in a model and performance of a time-continuous labelled spontaneous interaction. |
| Outcome: | a new study shows that the amount of context used in a model and performance is similar across models . the results show that knowledge about an appropriate context can reduce complexity and flexibility . |
How Users React to Proactive Voice Assistant Behavior While Driving (2020.lrec-1)
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| Challenge: | Nowadays Personal Assistants (PAs) are available in multiple environments and become increasingly popular to use via voice. |
| Approach: | They conducted a usability study in which 42 participants perceive proactive voice output in a Wizard-of-Oz study in . traffic density was varied during a highway drive and it included six in-car-specific use cases. |
| Outcome: | The proposed suggestions should not be obtrusive nor increase drivers’ cognitive load, while enhancing user experience. |