From a system developer's perspective, designing a spoken dialogue system can be a time-consuming and difficult process. A developer may spend a lot of time anticipating how a potential user might interact with the system and then deciding on the most appropriate system response. These decisions are encoded in a dialogue strategy, essentially a mapping between anticipated user inputs and appropriate system outputs. To reduce the time and effort associated with developing a dialogue strategy, recent work has concentrated on modelling the development of a dialogue strategy as a sequential decision problem. Using this model, reinforcement learning algorithms have been employed to generate dialogue strategies automatically. These algorithms learn strategies by interacting with simulated users. Some progress has been made with this method but a number of important challenges remain. For instance, relatively little success has been achieved with the large state representations that are typical of real-life systems. Another crucial issue is the time and effort associated with the creation of simulated users. In this thesis, I propose an alternative to existing reinforcement learning methods of dialogue strategy development. More specifically, I explore how XCS, an evolutionary reinforcement learning algorithm, can be used to find dialogue strategies that cover large state spaces. Furthermore, I suggest that hand-coded simulated users are sufficient for the learning of useful dialogue strategies. I argue that the use of evolutionary reinforcement learning and hand-coded simulated users is an effective approach to the rapid development of spoken dialogue strategies. Finally, I substantiate this claim by evaluating a learned strategy with real users. Both the learned strategy and a state-of-the-art hand-coded strategy were integrated into an end-to-end spoken dialogue system. The dialogue system allowed real users to make flight enquiries using a live database for an Edinburgh-based airline. The performance of the learned and hand-coded strategies were compared. The evaluation results show that the learned strategy performs as well as the hand-coded one (81% and 77% task completion respectively) but takes much less time to design (two days instead of two weeks). Moreover, the learned strategy compares favourably with previous user evaluations of learned strategies.

This thesis addresses the question of how speakers adapttheir language when they interact with a spoken dialoguesystem. In human–human dialogue, people continuously adaptto their conversational partners at different levels. Wheninteracting with computers, speakers also to some extent adapttheir language to meet (what they believe to be) theconstraints of the dialogue system. Furthermore, if a problemoccurs in the human–computer dialogue, patterns oflinguistic adaptation are often accentuated. In this thesis, we used an empirical approach in which aseries of corpora of human–computer interaction werecollected and analyzed. The systems used for data collectionincluded both fully functional stand-alone systems in publicsettings, and simulated systems in controlled laboratoryenvironments. All of the systems featured animated talkingagents, and encouraged users to interact using unrestrictedspontaneous language. Linguistic adaptation in the corpora wasexamined at the phonetic, prosodic, lexical, syntactic andpragmatic levels. Knowledge about users’linguistic adaptations can beuseful in the development of spoken dialogue systems. If we areable to adequately describe their patterns of occurrence (atthe different linguistic levels at which they occur), we willbe able to build more precise user models, thus improvingsystem performance. Our knowledge of linguistic adaptations canbe useful in at least two ways: first, it has been shown thatlinguistic adaptations can be used to identify (andsubsequently repair) errors in human–computer dialogue.Second, we can try to subtly influence users to behave in acertain way, for instance by implicitly encouraging a speakingstyle that improves speech recognition performance.

<p>This thesis addresses the question of how speakers adapttheir language when they interact with a spoken dialoguesystem. In human–human dialogue, people continuously adaptto their conversational partners at different levels. Wheninteracting with computers, speakers also to some extent adapttheir language to meet (what they believe to be) theconstraints of the dialogue system. Furthermore, if a problemoccurs in the human–computer dialogue, patterns oflinguistic adaptation are often accentuated.</p><p>In this thesis, we used an empirical approach in which aseries of corpora of human–computer interaction werecollected and analyzed. The systems used for data collectionincluded both fully functional stand-alone systems in publicsettings, and simulated systems in controlled laboratoryenvironments. All of the systems featured animated talkingagents, and encouraged users to interact using unrestrictedspontaneous language. Linguistic adaptation in the corpora wasexamined at the phonetic, prosodic, lexical, syntactic andpragmatic levels.</p><p>Knowledge about users’linguistic adaptations can beuseful in the development of spoken dialogue systems. If we areable to adequately describe their patterns of occurrence (atthe different linguistic levels at which they occur), we willbe able to build more precise user models, thus improvingsystem performance. Our knowledge of linguistic adaptations canbe useful in at least two ways: first, it has been shown thatlinguistic adaptations can be used to identify (andsubsequently repair) errors in human–computer dialogue.Second, we can try to subtly influence users to behave in acertain way, for instance by implicitly encouraging a speakingstyle that improves speech recognition performance.</p>

Recent advancements in the area of spoken language processing and the wide acceptance of portable devices, have attracted signicant interest in spoken dialogue systems.These conversational systems are man-machine interfaces which use natural language (speech) as the medium of interaction.In order to conduct dialogues, computers must have the ability to decide when and what information has to be exchanged with the users. The dialogue management module is responsible to make these decisions so that the intended task (such as ticket booking or appointment scheduling) can be achieved.Thus learning a good strategy for dialogue management is a critical task.In recent years reinforcement learning-based dialogue management optimization has evolved to be the state-of-the-art. A majority of the algorithms used for this purpose needs vast amounts of training data.However, data generation in the dialogue domain is an expensive and time consuming process. In order to cope with this and also to evaluatethe learnt dialogue strategies, user modelling in dialogue systems was introduced. These models simulate real users in order to generate synthetic data.Being computational models, they introduce some degree of modelling errors. In spite of this, system designers are forced to employ user models due to the data requirement of conventional reinforcement learning algorithms can learn optimal dialogue strategies from limited amount of training data when compared to the conventional algorithms. As a consequence of this, user models are no longer required for the purpose of optimization, yet they continue to provide a fast and easy means for quantifying the quality of dialogue strategies. Since existing methods for user modelling are relatively less realistic compared to real user behaviors, the focus is shifted towards user modelling by means of inverse reinforcement learning. Using experimental results, the proposed method's ability to learn a computational models with real user like qualities is showcased as part of this work.

In this thesis, we investigate dependency parsing for commercial application, namely for future integration in a dialogue system. To do this, we conduct several experiments on dialogue data to assess parser performance on this domain, and to improve this performance over a baseline. This work makes the following contributions: first, the creation and manual annotation of a gold-standard data set for dialogue data; second, a thorough error analysis of the data set, comparing neural network parsing to traditional parsing methods on this domain; and finally, various domain adaptation experiments show how parsing on this data set can be improved over a baseline.  We further show that dialogue data is characterized by questions in particular, and suggest a method for improving overall parsing on these constructions.

The purpose of this thesis is to develop and evaluate user simulators for a spoken dialogue system. Created simulators are operating on dialogue act level. We implemented a bigram simulator as a baseline system. Based on the baseline simulator, we created another bigram simulator that is trained on dialogue acts without slot values. The third implemented simulator is similar to an implemen- tation of a dialogue manager. It tracks its dialogue state and learns a dialogue strategy based on the state using supervised learning. The user simulators are implemented in Python 2.7, in ALEX framework for dialogue system development. Simulators are developed for PTICS application which operates in the domain of public transport information. Simulators are trained and evaluated using real human-machine dialogues collected with PTICS application. 1

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The work in this thesis addresses design and development of multimodal dialogue recommender systems for the home context-of-use. In the design part, two investigations on multimodal recommendation dialogue interaction in the home context are reported on. The first study gives implications for the design of dialogue system interaction including personalization and a three-entity multimodal interaction model accommodating dialogue feedback in order to make the interaction more efficient and successful. In the second study a dialogue corpus of movie recommendation dialogues is collected and analyzed, providing a characterization of such dialogues. We identify three initiative types that need to be addressed in a recommender dialogue system implementation: system-driven preference requests, userdriven information requests, and preference volunteering. Through the process of dialogue distilling, a dialogue control strategy covering system-driven preference requests from the corpus is arrived at. In the development part, an application-driven development process is adopted where reusable generic components evolve through the iterative and incremental refinement of dialogue systems. The Phase Graph Processor (PGP) design pattern is one such evolved component suggesting a phase-based control of dialogue systems. PGP is a generic and flexible micro architecture accommodating frequent change of requirements inherent of agile, evolutionary system development. As PGP has been used in a series of previous information-providing dialogue system projects, a standard phase graph has been established that covers the second initiative type; user-driven information requests. The phase graph is incrementally refined in order to provide user preference modeling, thus addressing the third initiative type, and multimodality as indicated by the user studies. In the iterative development of the multimodal recommender dialogue system MADFILM the phase graph is coupled with the dialogue control strategy in order to cater for the seamless integration of the three initiative types.

Task-oriented dialogue systems are a popular way for organisations to generate extra value both internally and for customers. Modern approaches for these dialogue systems that use neural networks to enable training directly on written dialogues are very data hungry, which complicates their implementation. Crowdsourcing is an attractive solution for generating this type of training data, but the method also comes with several difficulties. We introduce a new method for generating training data based on parallel crowdsourcing of dialogues, as well as crowdsourced quality review. We use this method to collect a small dataset that takes place within the domain bus driver-traveler. We believe that this method offers an efficient way to collect new, high-quality datasets. Hybrid Code Networks is a model for dialogue systems that combines a neural network with domain-specific knowledge, and thus requires a significantly smaller amount of training data than other similar dialogue systems to achieve comparable performance. By combining Hybrid Code Networks with our new method for generating training data, we believe that the threshold for implementing task-oriented dialogue systems on domains with insufficient training data can be lowered. We implement Hybrid Code Networks and train the implementation on the collected dataset and achieve good results. / Uppgiftsorienterade dialogsystem är ett populärt sätt för företag att generera extra värde både internt och för kunder. Moderna modeller för dessa dialogsystem som använder neurala nätverk för att möjliggöra träning direkt på skriftliga dialoger är väldigt datahungriga, vilket försvårar implementationen av dessa. Crowdsourcing är en attraktiv lösning för att generera denna typ av träningsdata, men metoden kommer även med flera svårigheter. Vi introducerar en ny metod för generering av träningsdata som bygger på parallell crowdsourcing av dialoger, samt crowdsourcad kvalitetsgranskning. Vi använder denna metod för att samla in ett litet dataset som utspelar sig inom domänen busschaufför-resenär. Vi menar att denna metod erbjuder ett effektivt sätt att samla in nya, högkvalitativa dataset. Hybrid Code Networks är en modell för dialogsystem som kombinerar ett neuralt nätverk med domänspecifik kunskap, och som på så sätt kräver en betydligt mindre mängd träningsdata än andra liknande dialogsystem för att uppnå jämförbar prestanda. Genom att kombinera Hybrid Code Networks med vår nya metod för generering av träningsdata menar vi att man kan sänka tröskeln för att implementera uppgiftsorienterade dialogsystem på domäner med otillräcklig träningsdata. Vi implementerar Hybrid Code Networks och tränar implementationen på det insamlade datasetet, och uppnår goda resultat.

This Bachelor thesis investigates the influence of prompt engineering and the integration of an Interaction Quality (IQ) feedback loop on the performance of ChatGPT-3.5 as a voice assistant. By analysing empirical data across multiple configurations, this study explores how these interventions affect response accuracy and efficiency. Findings suggest that prompt engineering tends to enhance system performance, though the benefits of the IQ feedback loop remain less clear and require further investigation. This study contributes to the field by delineating the potential for targeted modifications to improve dialogue system outputs in real-time applications.