Safe transfer learning for dialogue applications

International audienceIn this paper, we formulate the hypothesis that the first dialogues with a new user should be handle in a very conservative way, for two reasons : avoid user dropout; gather more successful dialogues to speedup the learning of the asymptotic strategy. To this extend, we propose to transfer a safe strategy to initiate the first dialogues

Reinforcement learning for personalized dialogue management

Language systems have been of great interest to the research community and have recently reached the mass market through various assistant platforms on the web. Reinforcement Learning methods that optimize dialogue policies have seen successes in past years and have recently been extended into methods that personalize the dialogue, e.g. take the personal context of users into account. These works, however, are limited to personalization to a single user with whom they require multiple interactions and do not generalize the usage of context across users. This work introduces a problem where a generalized usage of context is relevant and proposes two Reinforcement Learning (RL)-based approaches to this problem. The first approach uses a single learner and extends the traditional POMDP formulation of dialogue state with features that describe the user context. The second approach segments users by context and then employs a learner per context. We compare these approaches in a benchmark of existing non-RL and RL-based methods in three established and one novel application domain of financial product recommendation. We compare the influence of context and training experiences on performance and find that learning approaches generally outperform a handcrafted gold standard

Online learning and transfer for user adaptation in dialogue systems

International audienceWe address the problem of user adaptation in Spoken Dialogue Systems. The goal is to quickly adapt online to a new user given a large amount of dialogues collected with other users. Previous works using Transfer for Reinforcement Learning tackled this problem when the number of source users remains limited. In this paper, we overcome this constraint by clustering the source users: each user cluster, represented by its centroid, is used as a potential source in the state-of-the-art Transfer Reinforcement Learning algorithm. Our benchmark compares several clustering approaches , including one based on a novel metric. All experiments are led on a negotiation dialogue task, and their results show significant improvements over baselines

Investigating the impact of stakeholder collaboration on industry-wide enterprise risk management: a case study of the US aerial adventure industry

The aim of this study is to advance the knowledge and understanding of collaboration theory and risk management, and thereby develop a collaborative risk management framework to portray how stakeholder collaboration can lead to effective risk management in the aerial adventure industry. This study will outline the US aerial adventure industry’s paradoxical relationship with risk, representing a key ingredient whilst also raising questions over the long-term sustainability of the activity, due to a staggering increase in accidents. As a result, the industry attempts to create an illusion of risk and mitigate actual risk where possible through risk management. This study will thus argue that industry-wide stakeholder collaboration on risk management is the most suitable solution to the risk management conundrum in the industry. Whilst risk management and collaboration theory have been widely commented on in the literature, there is a gap in the knowledge in regards to combining the two for effective risk management. A gap exists in the knowledge on the results of a collaborative industry stakeholder approach to risk management, be it in the aerial adventure industry or elsewhere. This study seeks to improve risk management procedures within the industry through the introduction of industry-wide enterprise risk management (IERM), a modified version of enterprise risk management (ERM), a traditional financial risk management framework, with a strong focus on industry-wide stakeholder collaboration. Indeed, this study will argue IERM should be the cornerstone of the aerial adventure industry in combination with a proposed safety committee. A qualitative case study approach was employed to provide an in-depth understanding of how industry stakeholder collaboration may improve risk management. A combination of convenience, snowball and purposeful sampling techniques were employed. 20 interviews took place with key stakeholders from both the private, public and third sector participating, after which thematic analysis was used to analyse the data. The data will indicate a particularly dynamic industry aware of the need and keen to improve on stakeholder collaboration to improve its risk management procedures. However, a number of barriers are identified such as trust and a lack of infrastructure. Theoretical contributions come from the creation of the relational resource dependency framework as well as the Safety Committee Life Cycle model. This study will call for the creation of a safety committee at industry-level to facilitate stakeholder collaboration and thereby improve risk management procedures. For this to be effective, a need for the standard-writing organisations to merge was identified