A dialog state tracker is an important component in modern spoken dialog systems. We present an incremental dialog state tracker, based on LSTM networks. It directly uses automatic speech recognition hypotheses to track the state. We also present the key non-standard aspects of the model that bring its performance close to the state-of-the-art and experimentally analyze their contribution: including the ASR confidence scores, abstracting scarcely represented values, including transcriptions in the training data, and model averaging.
The only other incremental dialog tracker know to us is used in @cite_13 . In this paper, the authors describe an incremental dialog system for number dictation as a specific instance of their incremental dialog processing framework. To track the dialog state, they use a discourse modeling system which keeps track of confidence scores from semantic parses of the input; these are produced by a grammar-based semantic interpreter with a hand-coded context-free grammar. Unlike our system, it requires handcrafted grammar and an explicit semantic representation of the input. Using RNN for dialog state tracking has been proposed before @cite_16 @cite_20 . The dialog state tracker in @cite_16 uses an RNN, with a very elaborate architecture, to track the dialog state turn-by-turn. Similarly to our model, their model does not need an explicit semantic representation of the input. They also use a similar abstraction of low-occurring values (they call the technique tagged n-gram features''), which should result in better generalization on rare but well-recognized values. We use only 1-best ASR hypothesis and achieve near state-of-the-art results, while the other tracking models from the literature @cite_15 @cite_16 @cite_9 @cite_1 typically use the whole ASR SLU n-best list as an input.
@cite_9 @cite_1 @cite_15 @cite_16 @cite_13 @cite_20
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For robust spoken dialog management, various dialog state tracking methods have been proposed. Although discriminative models are gaining popularity due to their superior performance, generative models based on the Partially Observable Markov Decision Process model still remain attractive since they provide an integrated framework for dialog state tracking and dialog policy optimization. Although a straightforward way to fit a generative model is to independently train the component probability models, we present a gradient descent algorithm that simultaneously train all the component models. We show that the resulting tracker performs competitively with other top-performing trackers that participated in DSTC2. We present our work in Dialog State Tracking Challenge 5, the main task of which is to track dialog state on human-human conversations cross language. Firstly a probabilistic enhanced framework is used to represent sub-dialog, which consists of three parts, the input model for extracting features, the enhanced model for updating dialog state and the output model to give the tracking frame. Meanwhile, parallel language systems are proposed to overcome inaccuracy caused by machine translation for cross language testing. We also introduce a new iterative alignment method extended from our work in DSTC4. Furthermore, a slot-based score averaging method is introduced to build an ensemble by combining different trackers. Results of our DSTC5 system show that our method significantly improves tracking performance compared with baseline method. In spoken dialog systems, statistical state tracking aims to improve robustness to speech recognition errors by tracking a posterior distribution over hidden dialog states. This paper introduces two novel methods for this task. First, we explain how state tracking is structurally similar to web-style ranking, enabling mature, powerful ranking algorithms to be applied. Second, we show how to use multiple spoken language understanding engines (SLUs) in state tracking — multiple SLUs can expand the set of dialog states being tracked, and give more information about each, thereby increasing both recall and precision of state tracking. We evaluate on the second Dialog State Tracking Challenge; together these two techniques yield highest accuracy in 2 of 3 tasks, including the most difficult and general task. This paper describes a fully incremental dialogue system that can engage in dialogues in a simple domain, number dictation. Because it uses incremental speech recognition and prosodic analysis, the system can give rapid feedback as the user is speaking, with a very short latency of around 200ms. Because it uses incremental speech synthesis and self-monitoring, the system can react to feedback from the user as the system is speaking. A comparative evaluation shows that naive users preferred this system over a non-incremental version, and that it was perceived as more human-like. While belief tracking is known to be important in allowing statistical dialog systems to manage dialogs in a highly robust manner, until recently little attention has been given to analysing the behaviour of belief tracking techniques. The Dialogue State Tracking Challenge has allowed for such an analysis, comparing multiple belief tracking approaches on a shared task. Recent success in using deep learning for speech research motivates the Deep Neural Network approach presented here. The model parameters can be learnt by directly maximising the likelihood of the training data. The paper explores some aspects of the training, and the resulting tracker is found to perform competitively, particularly on a corpus of dialogs from a system not found in the training.