Providing personalized Internet services by means of context-aware spoken dialogue systems

The widespread use of new mobile technology implementing wireless communications enables a new type of advanced applications to access information services on the Internet. In order to provide services which meet the user needs through intelligent information retrieval, the system must sense and interpret the user environment and the communication context. Though context-awareness is vital to provide services adapted to the user preferences, it cannot be useful if such services are difficult to access. The development of spoken dialogue systems for these applications facilitates interaction in natural language with the environment which is also benefited from contextual information. In this paper, we propose a framework to develop context-aware dialogue systems that dynamically incorporate user specific requirements and preferences as well as characteristics about the interaction environment, in order to improve and personalize web information and services. We have identified the major components for context-aware dialogue systems and placed them within a general-purpose architecture. The framework also describes a representation mode based on a dialogue register in order to share information between the elements of the architecture, and incorporates statistical methodologies for dialogue management in order to reduce the effort required for both the implementation of a new system and the adaptation to a new task. We have evaluated our proposal developing a travel-planning system, and provide a detailed discussion of its positive influence in the quality of the interaction and the information and services provided.Research funded by projects CICYT TIN2011- 28620-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485), and DPS2008- 07029-C02-02.Publicad

Action Selection for Interaction Management: Opportunities and Lessons for Automated Planning

The central problem in automated planning---action selection---is also a primary topic in the dialogue systems research community, however, the nature of research in that community is significantly different from that of planning, with a focus on end-to-end systems and user evaluations. In particular, numerous toolkits are available for developing speech-based dialogue systems that include not only a method for representing states and actions, but also a mechanism for reasoning and selecting the actions, often combined with a technical framework designed to simplify the task of creating end-to-end systems. We contrast this situation with that of automated planning, and argue that the dialogue systems community could benefit from some of the directions adopted by the planning community, and that there also exist opportunities and lessons for automated planning

Solomon Islands: Essential aspects of governance for Aquatic Agricultural Systems in Malaita Hub

In late 2012, a governance assessment was carried out as part of the diagnosis phase of rollout of the CGIAR Aquatic Agricultural Systems Program in Malaita Hub in Solomon Islands. The purpose of the assessment was to identify and provide a basic understanding of essential aspects of governance related to Aquatic Agricultural Systems in general, and more specifically as a case study in natural resource management. The underlying principles of the approach we have taken are drawn from an approach known as “Collaborating for Resilience” (CORE), which is based on bringing all key stakeholders into a process to ensure that multiple perspectives are represented (a listening phase), that local actors have opportunities to influence each other’s understanding (a dialogue phase), and that ultimately commitments to action are built (a choice phase) that would not be possible through an outsider’s analysis alone. This report begins to address governance from an AAS perspective, using input from AAS households and other networked stakeholders. We attempt to summarize governance issues that are found not only within the community but also, and especially, those that are beyond the local level, both of which may need to be addressed by the AAS program

Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and back-off n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question-answer pair corpus and from pretrained word embeddings.Comment: 8 pages with references; Published in AAAI 2016 (Special Track on Cognitive Systems