Learning Dialogue Strategies from Older and Younger Simulated Users

Older adults are a challenging user group because their behaviour can be highly variable. To the best of our knowledge, this is the first study where dialogue strategies are learned and evaluated with both simulated younger users and simulated older users. The simulated users were derived from a corpus of interactions with a strict system-initiative spoken dialogue system (SDS). Learning from simulated younger users leads to a policy which is close to one of the dialogue strategies of the underlying SDS, while the simulated older users allow us to learn more flexible dialogue strategies that accommodate mixed initiative. We conclude that simulated users are a useful technique for modelling the behaviour of new user groups

Automatic annotation of context and speech acts for dialogue corpora

Richly annotated dialogue corpora are essential for new research directions in statistical learning approaches to dialogue management, context-sensitive interpretation, and context-sensitive speech recognition. In particular, large dialogue corpora annotated with contextual information and speech acts are urgently required. We explore how existing dialogue corpora (usually consisting of utterance transcriptions) can be automatically processed to yield new corpora where dialogue context and speech acts are accurately represented. We present a conceptual and computational framework for generating such corpora. As an example, we present and evaluate an automatic annotation system which builds ‘Information State Update' (ISU) representations of dialogue context for the Communicator (2000 and 2001) corpora of human-machine dialogues (2,331 dialogues). The purposes of this annotation are to generate corpora for reinforcement learning of dialogue policies, for building user simulations, for evaluating different dialogue strategies against a baseline, and for training models for context-dependent interpretation and speech recognition. The automatic annotation system parses system and user utterances into speech acts and builds up sequences of dialogue context representations using an ISU dialogue manager. We present the architecture of the automatic annotation system and a detailed example to illustrate how the system components interact to produce the annotations. We also evaluate the annotations, with respect to the task completion metrics of the original corpus and in comparison to hand-annotated data and annotations produced by a baseline automatic system. The automatic annotations perform well and largely outperform the baseline automatic annotations in all measures. The resulting annotated corpus has been used to train high-quality user simulations and to learn successful dialogue strategies. The final corpus will be made publicly availabl

Goal setting: The role of feedback, external incentives, resource availability, and situational constraints on employees’ performance.

Theoretical Background This research examines the process through which a goal oriented environment can impact on individual performance and specifically, it explores factors that are related with goal setting and the way they influence the employees’ performance outcome. The study places emphasis on an unstable and insecure working setting and explores those factors in a country that has been affected by the financial crisis to a large degree. In particular, the present study investigates the relationships among the amount and frequency of feedback that employees receive with their perception about their improvement of performance due to feedback. In addition, the reported research seeks to expand our knowledge on the effect of financial incentives that employees are offered on their performance enhancement. The final factor that this study investigates is the availability of resources and the presence of situational constraints, and their relationship with the promotion of employees’ job performance. The resources and constraints are distinguished into training, autonomy and work overload. Hypotheses In order to fully comprehend the aforementioned relationships 6 hypotheses were constructed. Specifically, it is hypothesised that: 1) The greater the amount of feedback the employees receive, the higher the level of their performance. 2) The greater the frequency of feedback employees receive, the higher the level of their performance. 3) The larger the amount of a monetary reward tied to a performance goal, the greater the employees’ performance. 4) There is a positive relationship between training and performance. 5) There is a positive relationship between job autonomy and the performance. 6) There is negative relationship between the work overload levels and the employees’ performance. Methodology The target industry consists of Greek private corporations that offer professional financial services and the study population is comprised of 115 Greek employees (N=115). The data was collected through a cross-section survey. The employees were invited via email to answer an online questionnaire that was constructed and stored through the Bristol Online Surveys (BOS) system. After the collection of data, the results were introduced into the Statistical Package for the Social Sciences (SPSS V23.0) and correlation, tabulation of data, descriptive statistics and hierarchical regression analysis were conducted. Results As hypothesised, the amount of feedback was significantly and positively related to the employees’ performance. Greater amount of job-relevant information about past actions given to the employees resulted in greater subsequent performance. In addition, the monetary incentives offered for goal attainment had a positive and significant relationship with performance. The study participants reported that their effort and performance can be enhanced when monetary rewards are tied to their performance goals and particularly, the higher the incentive, the better their performance. However, it is noteworthy that the positive relationship between the frequency of feedback and the employees’ performance levels is not supported by the results. Furthermore, they show that Greek employees are not dependent on training to acquire knowledge, skills and competencies in order to manage to advance their performance outcome. Moreover, the study results indicate that autonomy and control over the task performed are not significant to facilitate and advance employees’ performance. What is more, the study failed to support the assertion that work overload is negatively related to performance. The results did not provide credible evidence to support the hypothesis that the workload and the performance level are negatively associated. Finally, implications for managerial practice, limitations with regard to the study and implications for future work on feedback and on the provision of incentives are discussed

Single-Agent vs. Multi-Agent Techniques for Concurrent Reinforcement Learning of Negotiation Dialogue Policies

Abstract We use single-agent and multi-agent Reinforcement Learning (RL) for learning dialogue policies in a resource allocation negotiation scenario. Two agents learn concurrently by interacting with each other without any need for simulated users (SUs) to train against or corpora to learn from. In particular, we compare the Qlearning, Policy Hill-Climbing (PHC) and Win or Learn Fast Policy Hill-Climbing (PHC-WoLF) algorithms, varying the scenario complexity (state space size), the number of training episodes, the learning rate, and the exploration rate. Our results show that generally Q-learning fails to converge whereas PHC and PHC-WoLF always converge and perform similarly. We also show that very high gradually decreasing exploration rates are required for convergence. We conclude that multiagent RL of dialogue policies is a promising alternative to using single-agent RL and SUs or learning directly from corpora

Reducing Working Memory Load in Spoken Dialogue Systems

We evaluated two strategies for alleviating working memory load for users of voice interfaces: presenting fewer options per turn and providing confirmations. Forty-eight users booked appointments using nine different dialogue systems, which varied in the number of options presented and the confirmation strategy used. Participants also performed four cognitive tests and rated the usability of each dialogue system on a standardised questionnaire. When systems presented more options per turn and avoided explicit confirmation subdialogues, both older and younger users booked appointments more quickly without compromising task success. Users with lower information processing speed were less likely to remember all relevant aspects of the appointment. Working memory span did not affect appointment recall. Older users were slightly less satisfied with the dialogue systems than younger users. We conclude that the number of options is less important than an accurate assessment of the actual cognitive demands of the task at hand

Prediction and Realisation of Conversational Characteristics by Utilising Spontaneous Speech for Unit Selection

Unit selection speech synthesis has reached high levels of naturalness and intelligibility for neutral read aloud speech. However, synthetic speech generated using neutral read aloud data lacks all the attitude, intention and spontaneity associated with everyday conversations. Unit selection is heavily data dependent and thus in order to simulate human conversational speech, or create synthetic voices for believable virtual characters, we need to utilise speech data with examples of how people talk rather than how people read. In this paper we included carefully selected utterances from spontaneous conversational speech in a unit selection voice. Using this voice and by automatically predicting type and placement of lexical fillers and filled pauses we can synthesise utterances with conversational characteristics. A perceptual listening test showed that it is possible to make synthetic speech sound more conversational without degrading naturalness

Learning Dialogue Strategies from Older and Younger Simulated Users

Older adults are a challenging user group because their behaviour can be highly variable. To the best of our knowledge, this is the first study where dialogue strategies are learned and evaluated with both simulated younger users and simulated older users. The simulated users were derived from a corpus of interactions with a strict system-initiative spoken dialogue system (SDS). Learning from simulated younger users leads to a policy which is close to one of the dialogue strategies of the underlying SDS, while the simulated older users allow us to learn more flexible dialogue strategies that accommodate mixed initiative. We conclude that simulated users are a useful technique for modelling the behaviour of new user groups

Automatic Annotation of Context and Speech Acts for Dialogue Corpora.

Richly annotated dialogue corpora are essential for new research directions in statistical learning approaches to dialogue management, context-sensitive interpretation, and context-sensitive speech recognition. In particular, large dialogue corpora annotated with contextual information and speech acts are urgently required. We explore how existing dialogue corpora (usually consisting of utterance transcriptions) can be automatically processed to yield new corpora where dialogue context and speech acts are accurately represented. We present a conceptual and computational framework for generating such corpora. As an example, we present and evaluate an automatic annotation system which builds ‘Information State Update’ (ISU) representations of dialogue context for the Communicator (2000 and 2001) corpora of human–machine dialogues (2,331 dialogues). The purposes of this annotation are to generate corpora for reinforcement learning of dialogue policies, for building user simulations, for evaluating different dialogue strategies against a baseline, and for training models for context-dependent interpretation and speech recognition. The automatic annotation system parses system and user utterances into speech acts and builds up sequences of dialogue context representations using an ISU dialogue manager. We present the architecture of the automatic annotation system and a detailed example to illustrate how the system components interact to produce the annotations. We also evaluate the annotations, with respect to the task completion metrics of the original corpus and in comparison to hand-annotated data and annotations produced by a baseline automatic system. The automatic annotations perform well and largely outperform the baseline automatic annotations in all measures. The resulting annotated corpus has been used to train high-quality user simulations and to learn successful dialogue strategies. The final corpus will be made publicly available