Dialog State Tracking with Reinforced Data Augmentation
نویسندگان
چکیده
منابع مشابه
The Dialog State Tracking Challenge
In a spoken dialog system, dialog state tracking deduces information about the user’s goal as the dialog progresses, synthesizing evidence such as dialog acts over multiple turns with external data sources. Recent approaches have been shown to overcome ASR and SLU errors in some applications. However, there are currently no common testbeds or evaluation measures for this task, hampering progres...
متن کاملMarkovian Discriminative Modeling for Dialog State Tracking
Discriminative dialog state tracking has become a hot topic in dialog research community recently. Compared to generative approach, it has the advantage of being able to handle arbitrary dependent features, which is very appealing. In this paper, we present our approach to the DSTC2 challenge. We propose to use discriminative Markovian models as a natural enhancement to the stationary discrimin...
متن کاملThe Dialog State Tracking Challenge Series
C onversational systems are increasingly becoming a part of daily life, with examples including Apple's Siri, Google Now, Nuance Dragon Go, Xbox, and Cortana from Microsoft, and those from numerous startups. In the core of a conversation system is a key component called a dialog state tracker, which estimates the user's goal given all of the dialog history so far. For example, in a tourist info...
متن کاملComparative Error Analysis of Dialog State Tracking
A primary motivation of the Dialog State Tracking Challenge (DSTC) is to allow for direct comparisons between alternative approaches to dialog state tracking. While results from DSTC 1 mention performance limitations, an examination of the errors made by dialog state trackers was not discussed in depth. For the new challenge, DSTC 2, this paper describes several techniques for examining the err...
متن کاملDiscriminative state tracking for spoken dialog systems
In spoken dialog systems, statistical state tracking aims to improve robustness to speech recognition errors by tracking a posterior distribution over hidden dialog states. Current approaches based on generative or discriminative models have different but important shortcomings that limit their accuracy. In this paper we discuss these limitations and introduce a new approach for discriminative ...
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ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2020
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v34i05.6491