1 code implementation • SIGDIAL (ACL) 2021 • Niklas Rach, Carolin Schindler, Isabel Feustel, Johannes Daxenberger, Wolfgang Minker, Stefan Ultes
Despite the remarkable progress in the field of computational argumentation, dialogue systems concerned with argumentative tasks often rely on structured knowledge about arguments and their relations.
no code implementations • LREC 2022 • Annalena Aicher, Alisa Gazizullina, Aleksei Gusev, Yuri Matveev, Wolfgang Minker
The growing popularity of various forms of Spoken Dialogue Systems (SDS) raises the demand for their capability of implicitly assessing the speaker’s sentiment from speech only.
no code implementations • LREC 2022 • Annalena Aicher, Nadine Gerstenlauer, Isabel Feustel, Wolfgang Minker, Stefan Ultes
We evaluate the likeability and motivation of users to interact with the new system in a user study.
no code implementations • LREC 2022 • Annalena Aicher, Nadine Gerstenlauer, Wolfgang Minker, Stefan Ultes
Most systems helping to provide structured information and support opinion building, discuss with users without considering their individual interest.
no code implementations • LREC 2022 • Matthias Kraus, Nicolas Wagner, Wolfgang Minker
For creating a sound interactive personalization, we have developed an empathy-augmented dialogue strategy.
no code implementations • LREC 2022 • Annalena Aicher, Wolfgang Minker, Stefan Ultes
To build a well-founded opinion it is natural for humans to gather and exchange new arguments.
no code implementations • 6 Aug 2023 • Ye Liu, Stefan Ultes, Wolfgang Minker, Wolfgang Maier
In this work, we study dialogue scenarios that start from chit-chat but eventually switch to task-related services, and investigate how a unified dialogue model, which can engage in both chit-chat and task-oriented dialogues, takes the initiative during the dialogue mode transition from chit-chat to task-oriented in a coherent and cooperative manner.
no code implementations • 4 Jul 2023 • Ye Liu, Stefan Ultes, Wolfgang Minker, Wolfgang Maier
We contribute two efficient prompt models which can proactively generate a transition sentence to trigger system-initiated transitions in a unified dialogue model.
no code implementations • 24 Apr 2023 • Matthias Kraus, Ron Riekenbrauck, Wolfgang Minker
In this paper, we present the development of a corpus-based user simulator for training and testing proactive dialog policies.
no code implementations • 20 Dec 2022 • Matthias Kraus, Diana Betancourt, Wolfgang Minker
For this reason, a concept learning task scenario was observed where a robotic assistant proactively helped when negative user states were detected.
no code implementations • 25 Nov 2022 • Matthias Kraus, Nicolas Wagner, Ron Riekenbrauck, Wolfgang Minker
The next step for intelligent dialog agents is to escape their role as silent bystanders and become proactive.
no code implementations • 29 Sep 2022 • Ye Liu, Wolfgang Maier, Wolfgang Minker, Stefan Ultes
The pre-trained conversational models still fail to capture the implicit commonsense (CS) knowledge hidden in the dialogue interaction, even though they were pre-trained with an enormous dataset.
no code implementations • ICON 2021 • Ye Liu, Wolfgang Maier, Wolfgang Minker, Stefan Ultes
We utilize the pre-trained multi-context ConveRT model for context representation in a model trained from scratch; and leverage the immediate preceding user utterance for context generation in a model adapted from the pre-trained GPT-2.
no code implementations • RANLP 2021 • Ye Liu, Wolfgang Maier, Wolfgang Minker, Stefan Ultes
This paper presents an automatic method to evaluate the naturalness of natural language generation in dialogue systems.
no code implementations • 7 Sep 2021 • Ye Liu, Wolfgang Maier, Wolfgang Minker, Stefan Ultes
One challenge for dialogue agents is to recognize feelings of the conversation partner and respond accordingly.
no code implementations • 3 Mar 2021 • Waheed Ahmed Abro, Annalena Aicher, Niklas Rach, Stefan Ultes, Wolfgang Minker, Guilin Qi
Intent classifier model stacks BiLSTM with attention mechanism on top of the pre-trained BERT model and fine-tune the model for recognizing the user intent, whereas the argument similarity model employs BERT+BiLSTM for identifying system arguments the user refers to in his or her natural language utterances.
no code implementations • 7 Oct 2020 • Denis Dresvyanskiy, Elena Ryumina, Heysem Kaya, Maxim Markitantov, Alexey Karpov, Wolfgang Minker
In this paper, we present our contribution to ABAW facial expression challenge.
no code implementations • LREC 2020 • Louisa Pragst, Wolfgang Minker, Stefan Ultes
Paraphrasing is an important aspect of natural-language generation that can produce more variety in the way specific content is presented.
no code implementations • LREC 2020 • Juliana Miehle, Isabel Feustel, Julia Hornauer, Wolfgang Minker, Stefan Ultes
We use this corpus to estimate the elaborateness and the directness of each utterance.
no code implementations • LREC 2020 • Maria Schmidt, Wolfgang Minker, Steffen Werner
Finally, the users reacted significantly faster to proactive PA actions, which we interpret as less cognitive load compared to non-proactive behavior.
no code implementations • LREC 2020 • Matthias Kraus, Fabian Fischbach, Pascal Jansen, Wolfgang Minker
Depending on the way a recommendation is communicated influences the user{'}s perception of the system.
no code implementations • LREC 2020 • Niklas Rach, Yuki Matsuda, Johannes Daxenberger, Stefan Ultes, Keiichi Yasumoto, Wolfgang Minker
We present an approach to evaluate argument search techniques in view of their use in argumentative dialogue systems by assessing quality aspects of the retrieved arguments.
no code implementations • WS 2019 • Oleg Akhtiamov, Ingo Siegert, Alexey Karpov, Wolfgang Minker
Mixup is shown to be beneficial for merging acoustic data (extracted features but not raw waveforms) from different domains that allows us to reach a higher classification performance on human-machine AD and also for training a multipurpose neural network that is capable of solving both human-machine and adult-child AD problems.
no code implementations • IJCNLP 2017 • Louisa Pragst, Koichiro Yoshino, Wolfgang Minker, Satoshi Nakamura, Stefan Ultes
Defining all possible system actions in a dialogue system by hand is a tedious work.
Cultural Vocal Bursts Intensity Prediction Spoken Dialogue Systems
no code implementations • WS 2017 • Niklas Rach, Wolfgang Minker, Stefan Ultes
For estimating the Interaction Quality (IQ) in Spoken Dialogue Systems (SDS), the dialogue history is of significant importance.
no code implementations • LREC 2016 • Roman Sergienko, Muhammad Shan, Wolfgang Minker
The numerical experiments have shown that the simultaneous use of the novel proposed approaches (collectives of term weighting methods and the novel feature transformation method) allows reaching the high classification results with very small number of features.
no code implementations • LREC 2016 • Maxim Sidorov, Alex Schmitt, er, Eugene Semenkin, Wolfgang Minker
Emotion Recognition (ER) is an important part of dialogue analysis which can be used in order to improve the quality of Spoken Dialogue Systems (SDSs).
no code implementations • LREC 2014 • Maxim Sidorov, Christina Brester, Wolfgang Minker, Eugene Semenkin
Automated emotion recognition has a number of applications in Interactive Voice Response systems, call centers, etc.
no code implementations • LREC 2014 • Stefan Ultes, H{\"u}seyin Dikme, Wolfgang Minker
While Spoken Dialogue Systems have gained in importance in recent years, most systems applied in the real world are still static and error-prone.
no code implementations • LREC 2012 • Tobias Heinroth, Maximilian Grotz, Florian Nothdurft, Wolfgang Minker
Thus we present three enhancements towards a more sophisticated use of the ontology-based dialogue models and show how grammars may dynamically be adapted in order to understand intuitive user utterances.
no code implementations • LREC 2012 • Florian Nothdurft, Wolfgang Minker
We propose that not only creating multimodal output for the user is important, but to take multimodal input resources into account for the decision when and how to interact.
no code implementations • LREC 2012 • Alex Schmitt, er, Stefan Ultes, Wolfgang Minker
Standardized corpora are the foundation for spoken language research.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • LREC 2012 • Kseniya Zablotskaya, Fern Mart{\'\i}nez, o Fern{\'a}ndez, Wolfgang Minker
In this paper we also checked a hypothesis that differences in vocabulary of speakers yielding different verbal intelligence are sufficient enough for good classification results.
no code implementations • LREC 2012 • Sergey Zablotskiy, Alex Shvets, er, Maxim Sidorov, Eugene Semenkin, Wolfgang Minker
In this paper a method for the syllable concatenation and error correction is suggested and tested.
no code implementations • LREC 2012 • Kseniya Zablotskaya, Umair Rahim, Fern Mart{\'\i}nez, o Fern{\'a}ndez, Wolfgang Minker
All the dialogues were divided into three groups: H-H is a group of dialogues between higher verbal intelligence participants, L-L is a group of dialogues between lower verbal intelligence participant and L-H is a group of all the other dialogues.