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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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. Recently discriminative methods for tracking the state of a spoken dialog have been shown to outperform traditional generative models. This paper presents a new wordbased tracking method which maps directly from the speech recognition results to the dialog state without using an explicit semantic decoder. The method is based on a recurrent neural network structure which is capable of generalising to unseen dialog state hypotheses, and which requires very little feature engineering. The method is evaluated on the second Dialog State Tracking Challenge (DSTC2) corpus and the results demonstrate consistently high performance across all of the metrics. Statistical approaches to dialog state tracking synthesize information across multiple turns in the dialog, overcoming some speech recognition errors. When training a dialog state tracker, there is typically only a small corpus of well-matched dialog data available. However, often there is a large corpus of mis-matched but related data ‐ perhaps pertaining to different semantic concepts, or from a different dialog system. It would be desirable to use this related dialog data to supplement the small corpus of well-matched dialog data. This paper addresses this task as multi-domain learning, presenting 3 methods which synthesize data from different slots and different dialog systems. Since deploying a new dialog state tracker often changes the resulting dialogs in ways that are difficult to predict, we study how well each method generalizes to unseen distributions of dialog data. Our main result is the finding that a simple method for multi-domain learning substantially improves performance in highly mis-matched conditions. 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.