Maria Soledad López Gambino

• 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 beforeThis 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.…
• 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.