TY - JOUR A1 - Pinkas, Ronen T1 - On prayer and dialectic in modern Jewish philosophy BT - Hermann Cohen and Franz Rosenzweig JF - Religions N2 - This paper is founded on two philosophical assumptions. The first is that there is a difference between two patterns of recognition: the dialectical and the dialogical. The second assumption is that the origins of the dialogical pattern may be found in the relationship between human beings and God, a relationship in which prayer has a major role. The second assumption leads to the supposition that the emphasis of the dialogic approach on moral responsibility is theologically grounded. In other words, the relationship between humanity and God serves as a paradigm for human relationships. By focusing on Hermann Cohen and Franz Rosenzweig, in the context of prayer and dialectic, this paper highlights the complexity of these themes in modern Jewish thought. These two important philosophers utilize dialectical reasoning while also criticizing it and offering an alternative. The conclusions of their thought, in general, and their position on prayer, in particular, demonstrate a preference for a relational way of thinking over a dialectical one, but without renouncing the latter. KW - dialectic KW - dialogue KW - prayer KW - modern Jewish philosophy KW - religious existentialism Y1 - 2023 U6 - https://doi.org/10.3390/rel14080996 SN - 2077-1444 VL - 14 IS - 8 SP - 1 EP - 28 PB - MDPI CY - Basel ER - TY - THES A1 - López Gambino, Maria Soledad T1 - Time Buying in Task-Oriented Spoken Dialogue Systems N2 - This dissertation focuses on the handling of time in dialogue. Specifically, it investigates how humans bridge time, or “buy time”, when they are expected to convey information that is not yet available to them (e.g. a travel agent searching for a flight in a long list while the customer is on the line, waiting). It also explores the feasibility of modeling such time-bridging behavior in spoken dialogue systems, and it examines how endowing such systems with more human-like time-bridging capabilities may affect humans’ perception of them. The relevance of time-bridging in human-human dialogue seems to stem largely from a need to avoid lengthy pauses, as these may cause both confusion and discomfort among the participants of a conversation (Levinson, 1983; Lundholm Fors, 2015). However, this avoidance of prolonged silence is at odds with the incremental nature of speech production in dialogue (Schlangen and Skantze, 2011): Speakers often start to verbalize their contribution before it is fully formulated, and sometimes even before they possess the information they need to provide, which may result in them running out of content mid-turn. In this work, we elicit conversational data from humans, to learn how they avoid being silent while they search for information to convey to their interlocutor. We identify commonalities in the types of resources employed by different speakers, and we propose a classification scheme. We explore ways of modeling human time-buying behavior computationally, and we evaluate the effect on human listeners of embedding this behavior in a spoken dialogue system. Our results suggest that a system using conversational speech to bridge time while searching for information to convey (as humans do) can provide a better experience in several respects than one which remains silent for a long period of time. However, not all speech serves this purpose equally: Our experiments also show that a system whose time-buying behavior is more varied (i.e. which exploits several categories from the classification scheme we developed and samples them based on information from human data) can prevent overestimation of waiting time when compared, for example, with a system that repeatedly asks the interlocutor to wait (even if these requests for waiting are phrased differently each time). Finally, this research shows that it is possible to model human time-buying behavior on a relatively small corpus, and that a system using such a model can be preferred by participants over one employing a simpler strategy, such as randomly choosing utterances to produce during the wait —even when the utterances used by both strategies are the same. N2 - Die zentralen Themen dieser Arbeit sind Zeit und Dialog. Insbesondere wird untersucht, wie Menschen Zeit gewinnen oder „Zeit kaufen“, wenn sie Informationen übermitteln müssen, die ihnen noch nicht zur Verfügung stehen (z. B. ein Reisebüroangestellter, der in einer langen Liste nach einem Flug sucht, während der Kunde am Telefon wartet). Außerdem wird untersucht, ob die Modellierung eines solchen Zeitüberbrückungsverhaltens in gesprochenen Dialogsystemen möglich ist und wie solche Fähigkeiten die Benutzererfahrung beeinflussen. Wir erheben Gesprächsdaten und ermitteln, wie die Sprecher den Dialog am Laufen halten, während sie nach Informationen für ihre(n) Gesprächspartner(in) suchen. Wir identifizieren Gemeinsamkeiten in den Ressourcen, die von verschiedenen Sprechern verwendet werden und schlagen ein Klassifizierungsschema vor. Wir erforschen Strategien, menschliches „Zeitüberbrückung“ zu modellieren, und wir bewerten die Auswirkungen dieses Verhaltens in ein gesprochenes Dialogsystem auf menschliche Zuhörer. T2 - Zeitgewinn in aufgabenorientierten Sprachdialogsystemen KW - dialogue system KW - Dialogsystem KW - linguistics KW - Linguistik KW - speech KW - Sprache KW - dialogue KW - Dialog KW - time-buying KW - Zeitgewinn Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-592806 ER - TY - THES A1 - Galetzka, Fabian T1 - Investigating and improving background context consistency in neural conversation models N2 - Neural conversation models aim to predict appropriate contributions to a (given) conversation by using neural networks trained on dialogue data. A specific strand focuses on non-goal driven dialogues, first proposed by Ritter et al. (2011): They investigated the task of transforming an utterance into an appropriate reply. Then, this strand evolved into dialogue system approaches using long dialogue histories and additional background context. Contributing meaningful and appropriate to a conversation is a complex task, and therefore research in this area has been very diverse: Serban et al. (2016), for example, looked into utilizing variable length dialogue histories, Zhang et al. (2018) added additional context to the dialogue history, Wolf et al. (2019) proposed a model based on pre-trained Self-Attention neural networks (Vasvani et al., 2017), and Dinan et al. (2021) investigated safety issues of these approaches. This trend can be seen as a transformation from trying to somehow carry on a conversation to generating appropriate replies in a controlled and reliable way. In this thesis, we first elaborate the meaning of appropriateness in the context of neural conversation models by drawing inspiration from the Cooperative Principle (Grice, 1975). We first define what an appropriate contribution has to be by operationalizing these maxims as demands on conversation models: being fluent, informative, consistent towards given context, coherent and following a social norm. Then, we identify different targets (or intervention points) to achieve the conversational appropriateness by investigating recent research in that field. In this thesis, we investigate the aspect of consistency towards context in greater detail, being one aspect of our interpretation of appropriateness. During the research, we developed a new context-based dialogue dataset (KOMODIS) that combines factual and opinionated context to dialogues. The KOMODIS dataset is publicly available and we use the data in this thesis to gather new insights in context-augmented dialogue generation. We further introduced a new way of encoding context within Self-Attention based neural networks. For that, we elaborate the issue of space complexity from knowledge graphs, and propose a concise encoding strategy for structured context inspired from graph neural networks (Gilmer et al., 2017) to reduce the space complexity of the additional context. We discuss limitations of context-augmentation for neural conversation models, explore the characteristics of knowledge graphs, and explain how we create and augment knowledge graphs for our experiments. Lastly, we analyzed the potential of reinforcement and transfer learning to improve context-consistency for neural conversation models. We find that current reward functions need to be more precise to enable the potential of reinforcement learning, and that sequential transfer learning can improve the subjective quality of generated dialogues. N2 - Neuronale Konversationsmodelle versuchen einen angemessenen Beitrag zu einer (gegebenen) Konversation zu erzeugen, indem neuronale Netze auf Dialogdaten trainiert werden. Ein spezieller Forschungszweig beschäftigt sich mit den nicht-zielgeführten Dialogen, erstmals vorgestellt von Ritter et al. (2011): Das Team untersuchte die Aufgabe der Transformation einer Äußerung in eine angemessene Antwort. Im Laufe der Zeit hat dieser Zweig Dialogsystem-Ansätze hervorgebracht, die lange Konversationen und zusätzlichen Kontext verarbeiten können. Einen sinnvollen und angemessenen Beitrag zu einem Gespräch zu leisten, ist eine komplexe Aufgabe, und daher war die Forschung auf diesem Gebiet sehr vielfältig: Serban et al. (2016) untersuchten beispielsweise die Verwendung von Dialogverläufen variabler Länge, Zhang et al. (2018) fügten der Dialoggeschichte zusätzlichen Kontext hinzu, Wolf et al. (2019) schlugen ein Modell vor, das auf vortrainierten neuronalen Self-Attention Schichten basiert (Vasvani et al., 2017), und Dinan et al. (2021) untersuchten Ansätze zur Kontrolle von unangebrachten Inhalten, wie zum Beispiel Beleidigungen. Dieser Trend kann als Transformation gesehen werden, der vom Versuch, ein Gespräch irgendwie fortzuführen, hin zum kontrollierten und zuverlässigen Generieren angemessener Antworten reicht. In dieser Arbeit untersuchen wir den Aspekt der Kontextkonsistenz genauer, der ein Aspekt unserer Interpretation von einem angemessenen Konversationsbeitrag ist. Während der Untersuchungen haben wir einen neuen kontextbasierten Dialogdatensatz (KOMODIS) entwickelt, der sachlichen und meinungsbezogenen Kontext zu Dialogen kombiniert. Der KOMODIS Datensatz ist öffentlich verfügbar, und wir verwenden die Daten in dieser Arbeit, um neue Einblicke in die kontextunterstützte Dialoggenerierung zu gewinnen. Wir haben außerdem eine neue Methode zur Eingabe von Kontext auf Self-Attention basierenden neuronalen Netzen entwickelt. Dazu erörtern wir zunächst das Problem der begrenzten Eingabelänge für Sequenzen aus Wissensgraphen in solche Modelle, und schlagen eine effiziente Codierungsstrategie für strukturierten Kontext vor, die von Graph Neural Networks inspiriert ist (Gilmer et al., 2017), um die Komplexität des zusätzlichen Kontexts zu reduzieren. Wir diskutieren die Grenzen der Kontexterweiterung für neuronale Konversationsmodelle, untersuchen die Eigenschaften von Wissensgraphen und erklären, wie wir Wissensgraphen für unsere Experimente erstellen und erweitern können. Schließlich haben wir das Potenzial von Reinforcement Learning und Transfer Learning analysiert, um die Kontextkonsistenz für neuronale Konversationsmodelle zu verbessern. Wir stellen fest, dass aktuelle Reward Funktionen präziser sein müssen, um das Potenzial von Reinforcement Learning zu nutzen, und dass Sequential Transfer Learning die subjektive Qualität der generierten Dialoge verbessern kann. KW - conversational ai KW - neural conversation models KW - context consistency KW - gpt KW - conversation KW - dialogue KW - deep learning KW - knowledge graphs KW - Kontextkonsistenz KW - Konversation KW - Dialog KI KW - Deep Learning KW - Dialog KW - GPT KW - Wissensgraph KW - neuronale Konversationsmodelle Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-584637 ER - TY - JOUR A1 - Stockhorst, Stefanie T1 - Hippologischer Fachdiskurs und dialogische Fiktion BT - Gabriel von Danups rhetorische Strategien im Sonderlichen vnd Lesewürdigen Gesprech (1623) JF - Daphnis : Zeitschrift für deutsche Literatur und Kultur der frühen Neuzeit (1400-1750) N2 - This contribution analyses the textual strategies in Danup’s literary dialogue, which is enriched in many ways with literary topoi and rhetorical devices. It is, in fact, a specialised text on the art of horsemanship, which proves to be surprisingly innovative in this regard. However, it is not only relevant to the hippological, but also to the political culture of the early modern period. For the author updates a literary genre pattern, takes up literary traditions and uses aesthetic means for successful self-promotion as an expert. T2 - Hippological expert discourse and dialogical fiction - Gabriel von Danup’s rhetorical strategies in Sonderliches vnd Newes Lesewürdiges Gesprech (1623) KW - Dialogliteratur KW - Reitkunst KW - Selbstinszenierung KW - dialogue KW - dialogical literature KW - expert culture KW - knowledge politics KW - horsemanship KW - early modernity KW - rhetoric Y1 - 2018 SN - 0300-693X U6 - https://doi.org/10.1163/18796583-12340025 SN - 1879-6583 VL - 49 IS - 3 SP - 416 EP - 443 PB - Brill Rodopi CY - Leiden ER - TY - JOUR A1 - Kosman, Admiʾel T1 - The temptation in the garden of R. Hiyya bar Ashi and his wife JF - European Judaism N2 - The narrative in BT Kiddushin 81b about R. Hiyya bar Ashi tells of a sage who waged a battle with his Urge after he refrained from engaging in sexual relations with his wife. He, however, did not reveal to her the battle being waged within him, but rather pretended to be an ‘angel’. When his wife incidentally found it, she disguised herself as a harlot and set out to seduce him. After they had engaged in sexual relations, the rabbi wanted to commit suicide. The traditional readings view R. Hiyya as the hero of the tale. This article claims that the aim of the narrative is to present the rabbi as being carried away by dualistic-Christian conceptions. The article further argues that the topic of the narrative is not sexual relations, but dialogue. KW - asceticism KW - dialogue KW - evil inclination KW - gender KW - Judaism KW - sex KW - Talmud Y1 - 2017 U6 - https://doi.org/10.3167/ej.2017.500214 SN - 0014-3006 SN - 1752-2323 VL - 50 IS - 2 SP - 129 EP - 146 PB - Berghahn Journals CY - Brooklyn ER - TY - JOUR A1 - Mast, Vivien A1 - Falomir, Zoe A1 - Wolter, Diedrich T1 - Probabilistic reference and grounding with PRAGR for dialogues with robots JF - Zeitschrift für pädagogische Psychologie. N2 - In this paper, we present a system for effective referential human-robot communication in the face of perceptual deviation using the Probabilistic Reference And GRounding mechanism PRAGR and vague feature models based on prototypes. PRAGR can handle descriptions of arbitrary complexity including spatial relations and uses flexible concept assignment in generation and resolution of referring expressions for bridging conceptual gaps in referential robot-robot or human-robot interaction. We evaluate the benefit of using vague as compared to crisp properties regarding referential success and robustness towards perspective alignment error in referential robot-robot and human-robot communication. KW - Reference handling KW - symbol grounding KW - human-machine interaction KW - vagueness KW - perceptual deviation KW - dialogue Y1 - 2016 U6 - https://doi.org/10.1080/0952813X.2016.1154611 SN - 0952-813X SN - 1362-3079 VL - 28 SP - 889 EP - 911 PB - Hogrefe CY - Abingdon ER -