Academic literature on the topic 'Task-oriented dialogue'

The Principle of Parsimony states that by and large, agents try to complete tasks using as little effort as possible. This thesis demonstrates that the Principle of Parsimony operates in human task-oriented dialogue by showing the effects of Parsimony in a corpus of human dialogues about a map navigation task and by using the main points of the analysis in order to guide simulated conversations between two computer agents within the JAM system. It makes four major contributions: an analysis of 'communicative posture', or a range of choices in dialogue which can be characterised by decisions about how much effort to spend constructing one's utterances, leading to either careful or risky behaviour about different aspects of communication, an analysis of 'recovery strategies' which allow the participants to recover from failures which have been brought about due to risky postures, a heuristic model of belief which risks failing to capture the full meaning of the dialogue in favour of efficiency in a way which models human belief updating more plausibly than previous models, and a layered agent architecture which allows the simulated agents to make all of their decisions based on the Principle of Parsimony.

This thesis explores the implications of Lindblom's theory of Hyper- and Hypo-articulation (Lindblom, 1983, 1990) for word intelligibility and the likely application of phonological reduction processes in spontaneous discourse, using data from the HCRC Map Task Corpus. Lindblom claims that variability in articulatory clarity is a reflection of speakers' assessments of their listeners' information requirements: speakers hyper-articulate when listeners require maximum acoustic input from with information from other sources. To prevent speakers from over-economising to a point of unintelligibility, hypo-articulation is governed by a constraint of lexical distinctiveness: speakers hypo-articulate only while listeners are able to distinguish the target from competing lexical items. Three main questions are addressed. First, do the informational needs of the listener affect the articulatory clarity of words produced in spontaneous conversation? A series of intelligibility experiments shows that repeated mentions of landmark names are less intelligible than their introductory mentions, independent of which speaker utters either mention, and who can see the landmark on their map. Although the results can be interpreted as supporting Lindblom's view, textual Giveness (Prince, 1981) is shown to depend upon what the speaker knows, rather than what the speaker believes her listener to know. The reduction in clarity associated with an increase in available information is not necessarily listener-oriented as the H & H theory proposes. Secondly, do phonological processes such as word-final /d/-deletion or place assimilation contribute to intelligibility loss? Although reduction processes are found to be more prevalent in tokens from spontaneous discourse than in matched citation forms, they generally fail to account for effects of repetition. An increase in assimilation is found for repeated mentions of nasal-final stimuli in pre-velar position, but no effects is found for assimilation in pre-label position, or for word-final /d/-deletion, nor is an effect found for the duration of schwa in metrically Weak initial syllables of polysyllabic words.

This thesis presents a discussion of proposed structuring principles for dialogue, and tests them empirically using data from the HCRC Map Task Corpus. The concepts of Cooperation (as described by Grice, and as used more generally in Linguistics), Coordination, Collaboration, Parsimony, Risk and Effort are ex¬ amined, and empirically testable hypotheses are developed, with which we are able to evaluate the claims for these principles in the context of task-oriented dialogue. In order to test our hypotheses, we categorise the utterances in our database in terms of Risk and Effort. Unlike Discourse Analysis, Conversation Analysis or Dialogue Games, our approach is evaluative. The intent in our dialogue coding is not only to label what the speakers did, but also to assess it in terms of its appropriateness at that point in the dialogue: the system codes not only what people do, but also what they don't do. Therefore, our system marks both the presence and absence of dialogue attributes. The hypotheses derived from the structuring concepts were statistically tested on the data produced by the coding system. The results produced by the empiri¬ cal tests showed a relationship between dialogue errors and task errors, but not between increased effort and increased task success. The importance of matched effort was also demonstrated, as dialogue pairs who invested similar amounts of effort produced better task results. Dialogue pairs also produced better results over time, which we argue is due to the focusing of effort. Participants work out where their effort should be channelled so that they can increase risk-taking where problems have not occurred, and decrease risk-taking where problems have occurred. These results suggest that interactants' behaviour follows a Principle of Least Individual Effort, which we argue subsumes the Principle of Parsimony and thus the Risk-Effort Trade-Olf. We reject the Principle of Least Collaborative Effort because although the empirical result of high effort not being associated with task success supports this principle in theory, we argue that its motivation is not supported in practice. The empirical work also distinguishes between what we term 'Gricean Cooperation', and folklinguistic notions of Cooperation found in the literature. In general terms, Gricean Cooperation predicted the same type of effort-minimising behaviour as the Principle of Least Individual Effort, and was thus supported by the empirical work. However, the concept of 'helpfulness' suggested by more general uses of Cooperation made predictions which were in conflict with those of the Principle of Least Individual Effort, and were found not to be supported.

Les besoins croissants en formation et en entrainement au travail d’équipe ont motivé l’utilisationd’Environnements de réalité Virtuelle Collaboratifs de Formation (EVCF) qui permettent aux utilisateurs de travailler avec des agents autonomes pour réaliser une activité collective. L’idée directrice est que la coordination efficace entre les membres d’une équipe améliore la productivité et réduit les erreurs individuelles et collectives. Cette thèse traite de la mise en place et du maintien de la coordination au sein d’une équipe de travail composée d’agents et d’humains interagissant dans un EVCF.L’objectif de ces recherches est de doter les agents virtuels de comportements conversationnels permettant la coopération entre agents et avec l’utilisateur dans le but de réaliser un but commun.Nous proposons une architecture d’agents Collaboratifs et Conversationnels, dérivée de l’architecture Belief-Desire-Intention (C2-BDI), qui gère uniformément les comportements délibératifs et conversationnels comme deux comportements dirigés vers les buts de l’activité collective. Nous proposons un modèle intégré de la coordination fondé sur l’approche des modèles mentaux partagés, afin d’établir la coordination au sein de l’équipe de travail composée d’humains et d’agents. Nous soutenons que les interactions en langage naturel entre les membres d’une équipe modifient les modèles mentaux individuels et partagés des participants. Enfin, nous décrivons comment les agents mettent en place et maintiennent la coordination au sein de l’équipe par le biais de conversations en langage naturel. Afin d’établir un couplage fort entre la prise de décision et le comportement conversationnel collaboratif d’un agent, nous proposons tout d’abord une approche fondée sur la modélisation sémantique des activités humaines et de l’environnement virtuel via le modèle mascaret puis, dans un second temps, une modélisation du contexte basée sur l’approche Information State. Ces représentations permettent de traiter de manière unifiée les connaissances sémantiques des agents sur l’activité collective et sur l’environnement virtuel ainsi que des informations qu’ils échangent lors de dialogues.Ces informations sont utilisées par les agents pour la génération et la compréhension du langage naturel multipartite. L’approche Information State nous permet de doter les agents C2BDI de capacités communicatives leur permettant de s’engager pro-activement dans des interactions en langue naturelle en vue de coordonner efficacement leur activité avec les autres membres de l’équipe. De plus, nous définissons les protocoles conversationnels collaboratifs favorisant la coordination entre les membres de l’équipe. Enfin, nous proposons dans cette thèse un mécanisme de prise de décision s’inspirant de l’approche BDI qui lie les comportements de délibération et de conversation des agents. Nous avons mis en oeuvre notre architecture dans trois différents scénarios se déroulant dans des EVCF. Nous montrons que les comportements conversationnels collaboratifs multipartites des agents C2BDI facilitent la coordination effective de l’utilisateur avec les autres membres de l’équipe lors de la réalisation d’une tâche partagée<br>Growing needs of educational and training requirements motivate the use of collaborative virtual environments for training (CVET) that allows human users to work together with autonomous agents to perform a collective activity. The vision is inspired by the fact that the effective coordination improves productivity, and reduces the individual and team errors. This work addresses the issue of establishing and maintaining the coordination in a mixed human-agent teamwork in the context of CVET. The objective of this research is to provide human-like conversational behavior of the virtual agents in order to cooperate with a user and other agents to achieve shared goals.We propose a belief-desire-intention (BDI) like Collaborative Conversational agent architecture(C2BDI) that treats both deliberative and conversational behaviors uniformly as guided by the goal-directed shared activity. We put forward an integrated model of coordination which is founded on the shared mental model based approaches to establish coordination in a human-agent teamwork. We argue that natural language interaction between team members can affect and modify the individual and shared mental models of the participants. Finally, we describe the cultivation of coordination in a mixed human-agent teamwork through natural language conversation. In order to establish the strong coupling between decision making and the collaborative conversational behavior of the agent, we propose first, the Mascaret based semantic modeling of human activities and the VE, and second, the information state based context model. This representation allows the treatment of semantic knowledge of the collaborative activity and virtual environment, and information exchanged during the dialogue conversation in a unified manner. This knowledge can be used by the agent for multiparty natural language processing (understanding and generation) in the context of the CEVT. To endow the communicative capabilities to C2BDI agent, we put forward the information state based approach for the natural language processing of the utterances. We define collaborative conversation protocols that ensure the coordination between team members. Finally, in this thesis, we propose a decision making mechanism, which is inspired by the BDI based approach and provides the interleaving between deliberation and conversational behavior of the agent. We have applied the proposed architecture to three different scenarios in the CVET. We found that the multiparty collaborative conversational behavior of C2BDI agent is more constructive and facilitates the user to effectively coordinate with other team members to perform a shared task

The thesis seeks to develop an account of collaborative activities within the framework of ecological realism—an approach to psychology developed by James J. Gibson in the course of work on visual perception. Two main questions are addressed; one ontological, and one methodological. The ontological question is: given that collaborative activities take place within an environment, what kinds of structure must this environment contain? The response emphasizes the importance of relations which exist between entities, and which connect a given perceiver-actor with the other objects and individuals in its surroundings, and with the relations between those entities. It is held that activities take place within a field of relations. This description draws on the radical empiricist doctrine that relations are real, are external, and are directly perceivable. The present proposal insists that, in addition to being directly perceivable, relations can also be directly acted upon: throwing a ball for a dog is acting on a relation between dog and ball in space. The relational field account of collaboration naturally extends to an account of speaking: people, through their history of acting in an environment populated by other speakers, come to stand in a set of relations with objects and events around them, and these relations can be directly acted upon by others through the use of verbal actions. Verbal actions serve to direct the attention of others to relevant aspects of the environment, and this allows us as speakers to coordinate and manage one another’s activity. The methodological question is this: granting that the environment may be structured as a field of relations, how are we to conduct our empirical investigations, such that we can ask precise questions which lead to useful insights about how a given collaborative activity is carried out in practice? The central issue here concerns the concept of the task. Psychologists are in the habit of using this term quite loosely, to denote the actions of an individual or a group, in a laboratory or outside. This creates confusion in discussions of collaborative phenomena: who is the agent of a ‘collaborative task’? The definition offered here states that a task is a researcher-defined unit of study that corresponds to a change in the structure of the environment that has a characteristic pattern and that is meaningful from the first-person perspective of a particular actor. On this definition, the task is a tool that allows ecological psychologists to carve up the problem space into specific, tractable questions; the task is the equivalent of the cognitivist’s mental module. Task-oriented psychology encourages us to ask the question: which specific resources is the individual making use of in controlling this particular activity? The methodology is developed through an examination of the alarm calling behaviour of vervet monkeys, which is explained in terms of actions on the relational field, and through an analysis of corpus data from a laboratory-based collaborative assembly game. The relational field model promises to provide a way of studying social and collaborative activities on ecological realist principles. The concluding chapter identifies two particular areas in which the model might fruitfully be developed: in the study of learning, and in the theory of designing objects and spaces for interaction.

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.<br>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.