Recurrent Polynomial Network for Dialogue State Tracking
نویسندگان
چکیده
منابع مشابه
Recurrent Polynomial Network for Dialogue State Tracking
Dialogue state tracking (DST) is a process to estimate the distribution of the dialogue states as a dialogue progresses. Recent studies on constrained Markov Bayesian polynomial (CMBP) framework take the first step towards bridging the gap between rule-based and statistical approaches for DST. In this paper, the gap is further bridged by a novel framework – recurrent polynomial network (RPN). R...
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Recently, constrained Markov Bayesian polynomial (CMBP) has been proposed as a data-driven rule-based model for dialog state tracking (DST). CMBP is an approach to bridge rule-based models and statistical models. Recurrent Polynomial Network (RPN) is a recent statistical framework taking advantages of rulebased models and can achieve state-ofthe-art performance on the data corpora of DSTC-3, ou...
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This paper discusses models for dialogue state tracking using recurrent neural networks (RNN). We present experiments on the standard dialogue state tracking (DST) dataset, DSTC2 [6]. On the one hand, RNN models became state of the art in DST, on the other hand, most state-of-the-art models are only turn-based and require dataset-specific preprocessing (e.g. DSTC2-specific) in order to achieve ...
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ژورنال
عنوان ژورنال: Dialogue & Discourse
سال: 2016
ISSN: 2152-9620
DOI: 10.5087/dad.2016.303