Incremental Learning from Scratch for Task-Oriented Dialogue Systems

Clarifying user needs is essential for existing task-oriented dialogue systems. However, in real-world applications, developers can never guarantee that all possible user demands are taken into account in the design phase. Consequently, existing systems will break down when encountering unconsidered user needs. To address this problem, we propose a novel incremental learning framework to design task-oriented dialogue systems, or for short Incremental Dialogue System (IDS), without pre-defining the exhaustive list of user needs. Specifically, we introduce an uncertainty estimation module to evaluate the confidence of giving correct responses. If there is high confidence, IDS will provide responses to users. Otherwise, humans will be involved in the dialogue process, and IDS can learn from human intervention through an online learning module. To evaluate our method, we propose a new dataset which simulates unanticipated user needs in the deployment stage. Experiments show that IDS is robust to unconsidered user actions, and can update itself online by smartly selecting only the most effective training data, and hence attains better performance with less annotation cost.Comment: ACL201

Emergent Innovation—a Socio-Epistemological Innovation Technology. Creating Profound Change and Radically New Knowledge as Core Challenges in Knowledge Management

This paper introduces an alternative approach to innovation: Emergent Innovation. As opposed to radical innovation Emergent Innovation finds a balance and integrates the demand both for radically new knowledge and at the same time for an organic development from within the organization. From a knowledge management perspective one can boil down this problem to the question of how to cope with the new and with profound change in knowledge. This question will be dealt with in the first part of the paper. As an implication the alternative approach of Emergent Innovation will be presented in the second part: this approach looks at innovation as a socio-epistemological process of “learning from the future”.\ud Keywords:\ud Innovation, radical innovation, emergent innovation, knowledge creation, change

Emergent Innovation and Sustainable Knowledge Co-creation. A Socio-Epistemological Approach to “Innovation from within”

Innovation has become one of the most important issues in modern knowledge society. As opposed to radical innovation this paper introduces the concept of Emergent Innovation: this approach tries to balance and integrate the demand both for radically new knowledge and at the same time for an organic development from within the organization. From a more general perspective one can boil down this problem to the question of how to cope with the new and with profound change (in knowledge). This question will be dealt with in the first part of the paper. As an implication the alternative approach of Emergent Innovation will be presented in the second part: this approach looks at innovation as a socio-epistemological process of “learning from the future” in order to create (radically) new knowledge in a sustainable and “organic” manner. Implications for knowledge society will be discussed.Knowledge society; (radical vs. incremental) innovation; emergent innovation; knowledge creation; change

End-to-End Goal-Oriented Conversational Agent for Risk Awareness

Traditional development of goal-oriented conversational agents typically require a lot of domain-specific handcrafting, which precludes scaling up to different domains; end-to-end systems would escape this limitation because they can be trained directly from dialogues. The very promising success recently obtained in end-to-end chatbots development could carry over to goal-oriented settings: applying deep learning models for building robust and scalable goal-oriented dialog systems directly from corpora of conversations is a challenging task and an open research area. For this reason, I decided that it would have been more relevant in the context of a master's thesis to experiment and get acquainted with these new promising methodologies - although not yet ready for production - rather than investing time in hand-crafting dialogue rules for a domain-specific solution. My thesis work had the following macro objectives: (i) investigate the latest research works concerning goal-oriented conversational agents development; (ii) choose a reference study, understand it and implement it with an appropriate technology; (iii) apply what learnt to a particular domain of interest. As a reference framework I chose the end-to-end memory networks (MemN2N) (Sukhbaatar et al., 2015) because it has proven to be particularly promising and has been used as a baseline for many recent works. Not having real dialogues available for training though, I took care of synthetically generating a corpora of conversations, taking a cue from the Dialog bAbI dataset for restaurant reservations (Bordes et al., 2016) and adapting it to the new domain of interest of risk awareness. Finally, I built a simple prototype which exploited the pre-trained dialog model in order to advise users about risk through an anthropomorphic talking avatar interface

Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis

Exploring data requires a fast feedback loop from the analyst to the system, with a latency below about 10 seconds because of human cognitive limitations. When data becomes large or analysis becomes complex, sequential computations can no longer be completed in a few seconds and data exploration is severely hampered. This article describes a novel computation paradigm called Progressive Computation for Data Analysis or more concisely Progressive Analytics, that brings at the programming language level a low-latency guarantee by performing computations in a progressive fashion. Moving this progressive computation at the language level relieves the programmer of exploratory data analysis systems from implementing the whole analytics pipeline in a progressive way from scratch, streamlining the implementation of scalable exploratory data analysis systems. This article describes the new paradigm through a prototype implementation called ProgressiVis, and explains the requirements it implies through examples.Comment: 10 page