55,742 research outputs found
Tracking recurrent concepts using context
The problem of recurring concepts in data stream classification is a special case of concept drift where concepts may reappear. Although several existing methods are able to learn in the presence of concept drift, few consider contextual information when tracking recurring concepts. Nevertheless, in many real-world scenarios context information is available and can be exploited to improve existing approaches in the detection or even anticipation of recurring concepts. In this work, we propose the extension of existing approaches to deal with the problem of recurring concepts by reusing previously learned decision models in situations where concepts reappear. The different underlying concepts are identified using an existing drift detection method, based on the error-rate of the learning process. A method to associate context information and learned decision models is proposed to improve the adaptation to recurring concepts. The method also addresses the challenge of retrieving the most appropriate concept for a particular context. Finally, to deal with situations of memory scarcity, an intelligent strategy to discard models is proposed. The experiments conducted so far, using synthetic and real datasets, show promising results and make it possible to analyze the trade-off between the accuracy gains and the learned models storage cost
Dialogue history integration into end-to-end signal-to-concept spoken language understanding systems
This work investigates the embeddings for representing dialog history in
spoken language understanding (SLU) systems. We focus on the scenario when the
semantic information is extracted directly from the speech signal by means of a
single end-to-end neural network model. We proposed to integrate dialogue
history into an end-to-end signal-to-concept SLU system. The dialog history is
represented in the form of dialog history embedding vectors (so-called
h-vectors) and is provided as an additional information to end-to-end SLU
models in order to improve the system performance. Three following types of
h-vectors are proposed and experimentally evaluated in this paper: (1)
supervised-all embeddings predicting bag-of-concepts expected in the answer of
the user from the last dialog system response; (2) supervised-freq embeddings
focusing on predicting only a selected set of semantic concept (corresponding
to the most frequent errors in our experiments); and (3) unsupervised
embeddings. Experiments on the MEDIA corpus for the semantic slot filling task
demonstrate that the proposed h-vectors improve the model performance.Comment: Accepted for ICASSP 2020 (Submitted: October 21, 2019
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
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