A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

Exploiting Cognitive Structure for Adaptive Learning

Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e.g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items. To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. By viewing path recommendation as a Markov Decision Process and applying an actor-critic algorithm, CSEAL can sequentially identify the right learning items to different learners. Specifically, we first utilize a recurrent neural network to trace the evolving knowledge levels of learners at each learning step. Then, we design a navigation algorithm on the knowledge structure to ensure the logicality of learning paths, which reduces the search space in the decision process. Finally, the actor-critic algorithm is used to determine what to learn next and whose parameters are dynamically updated along the learning path. Extensive experiments on real-world data demonstrate the effectiveness and robustness of CSEAL.Comment: Accepted by KDD 2019 Research Track. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19

Survey on Evaluation Methods for Dialogue Systems

In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class

Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time

Crowd-powered conversational assistants have been shown to be more robust than automated systems, but do so at the cost of higher response latency and monetary costs. A promising direction is to combine the two approaches for high quality, low latency, and low cost solutions. In this paper, we introduce Evorus, a crowd-powered conversational assistant built to automate itself over time by (i) allowing new chatbots to be easily integrated to automate more scenarios, (ii) reusing prior crowd answers, and (iii) learning to automatically approve response candidates. Our 5-month-long deployment with 80 participants and 281 conversations shows that Evorus can automate itself without compromising conversation quality. Crowd-AI architectures have long been proposed as a way to reduce cost and latency for crowd-powered systems; Evorus demonstrates how automation can be introduced successfully in a deployed system. Its architecture allows future researchers to make further innovation on the underlying automated components in the context of a deployed open domain dialog system.Comment: 10 pages. To appear in the Proceedings of the Conference on Human Factors in Computing Systems 2018 (CHI'18

The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems

This paper introduces the Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This provides a unique resource for research into building dialogue managers based on neural language models that can make use of large amounts of unlabeled data. The dataset has both the multi-turn property of conversations in the Dialog State Tracking Challenge datasets, and the unstructured nature of interactions from microblog services such as Twitter. We also describe two neural learning architectures suitable for analyzing this dataset, and provide benchmark performance on the task of selecting the best next response.Comment: SIGDIAL 2015. 10 pages, 5 figures. Update includes link to new version of the dataset, with some added features and bug fixes. See: https://github.com/rkadlec/ubuntu-ranking-dataset-creato

Towards responsive Sensitive Artificial Listeners

This paper describes work in the recently started project SEMAINE, which aims to build a set of Sensitive Artificial Listeners – conversational agents designed to sustain an interaction with a human user despite limited verbal skills, through robust recognition and generation of non-verbal behaviour in real-time, both when the agent is speaking and listening. We report on data collection and on the design of a system architecture in view of real-time responsiveness

Education Workforce Initiative: Initial Research

The purpose of this initial research is to offer evidenced possibilities in the key areas of education workforce roles, recruitment, training, deployment and leadership, along with suggested areas for further research to inform innovation in the design and strengthening of the public sector education workforce. The examples described were identified through the process outlined in the methodology section of this report, whilst we recognise that separation of examples from their context is problematic – effective innovations are highly sensitive to context and uncritical transfer of initiatives is rarely successful. The research aims to support the Education Workforce Initiative (EWI) in moving forward with engaging education leaders and other key actors in radical thinking around the design and strengthening of the education workforce to meet the demands of the 21st century. EWI policy recommendations will be drawn from a number of country level workforce reform activities and research activity associated with the production of an Education Workforce Report (EWR). This research has informed the key questions, approach and structure of the EWR as outlined in the Education Workforce Report Proposal. Issues pertaining to teaching and learning in primary and secondary education are at the centre of the research reported here; the focus is on moving towards schools as safe places where all children/ young people are able to engage in meaningful activity. The majority of the evidence shared here relates to teachers and school leaders; evidence on learning support staff, district officials and the wider education workforce is scant. Many of the issues examined are also pertinent to the early childhood care and education sector but these are being examined in depth by the Early Childhood Workforce Initiative. Resourcing for the Education Workforce was out of scope of this initial research but the EC recognises, as outlined in the Learning Generation Report, that provision of additional finance is a critical factor in achieving a sustainable, strong and well-motivated education workforce, particularly but not exclusively, in low and middle income countries. The next stage of EWI work will consider the relative costs of current initiatives and modelling of the cost implications of proposed reforms. EWI aims to complement the work on teacher policy design and teacher career frameworks (including salary structures) being undertaken by other bodies and institutions such as Education International, the International Task Force on Teachers for 2030 and the Teachers’ Alliance, most particularly by bringing a focus on school and district leadership, the role of Education Support Professionals (ESPs) and inter-agency working