It was his son who converted the building into a Zen temple of the Rinzai school. The temple was burned down several times during the Onin War. The pond in front of it is called Kyoko-chi. There are many islands and stones on the pond that represent the Buddhist creation story. Example of Wikipedia document translation of Japanese. There is an example dialogue with the system in Figure 3. U1: S1: Please tell me about the Golden Pavilion. Information query The Golden Pavilion is one of the buildings in the Rokuon-ji in Kyoto, and is the main attraction of the temple sites.
The entire pavilion except the basement floor is covered with pure gold leaf. U2: When was it built? U3: Then, please tell me its history. Information query Figure 3. Here, xi and wi are occurrence counts for noun i. The matching score M atch W, d is calculated as the product of these two vectors.
Heuristic rules that consist of Japanese cue phrases were hand-crafted in this work to classify the types of the user utterances. Each rule maps the input to the corresponding type2. Six types of NEs that correspond to the target question types were labeled a priori. We implemented an answer extraction module that consists of commonlyused procedures. The system extracts NEs or answer candidates N Ei that correspond to the wh-question type from the retrieved documents. Here, Senti denotes the sentence containing N Ei and Bunsetsui denotes a set of bunsetsus3 that have a dependency relationship with the bunsetsu containing N Ei.
M Si : The number of times nouns in the input wh-question appear in Senti. The retrieval result would be severely damaged if some important information was not correctly recognized. Even if the first-best hypothesis includes an error, the correct recognition result may be included in the N-best hypotheses. We thus use the N-best hypotheses of the ASR result to create a search query and extract an answer. Users of interactive retrieval systems tend to make utterances that include anaphoric expressions4.
In these cases, it is impossible to extract the correct answer by only using the current utterance. The simplest way is to use all the utterances made by the current user. However, this might also add inappropriate contexts because the topic might have changed during the session. De Boni Boni and Manandhar, proposed an algorithm for detecting topics based on similarity of question sequences in a question-answering task with typed text input. We track the topic using metadata from the KB or the title of the document.
The topic is tracked using the current document that have been focused on, which usually correspond to a sightseeing spot or Wikipedia entry. Thus, the occurrence counts of nouns within the context weighted by their ASR CM are incorporated when generating a search query Wi. Users ranged in a wide variety of ages from children to seniors and apparently had little expe- Online Learning of Bayes Risk-Based Optimization 37 rience with using spoken dialogue systems.
During an exhibition that lasted three months, 2, dialogue sessions were collected. A trigram language model for the ASR system was trained using the KB, a dialogue corpus from a different domain, and Web texts Misu and Kawahara, a. The vocabulary size of the model was 55, The average word accuracy for the information queries and the wh-questions was We constructed a test set using 1, in-domain utterances that include 1, information queries as well as wh-questions collected over a particular period of time the first one third of the period.
The average length number of words of the utterances was 4. Confirmation is indispensable to avoid inappropriate documents from being presented especially when the score for retrieval is low. This kind of choices in conventional studies were made based on combinations of empirical knowledge, such as the ASR performance and the task type. However, hand-crafting heuristic rules is usually costly, and subtle changes in choices can seriously affect the performance of the whole system.
Therefore, we propose a formulation where the above choices are optimized through online learning. Bayes risk L dj W is minimized in general pattern classification to determine the optimal class dj for an input W. These classes d1 , d2 ,. We assume the loss function among classes is the same, and we extend the framework to reward negative loss; l dj di 2. The dual cost method Dohsaka et al. This chapter focuses on response generation, and we do not deal with optimization in search query generation in Misu and Kawahara, The possible response set Res includes answering Ans di , presentation P res di , confirmation Conf di , and rejection Rej di.
P res di denotes a simple presentation of document di , which is actually made by summarizing it. Conf di is an explicit confirmation5 for presenting document di. Rej denotes a rejection: the system gives up making a response from document di and request the user for a rephrasal. This flow is illustrated in Figure 4.
We define the Bayes risk based on the reward for success, the penalty for failure, and the probability of success, which is approximated by the confidence measure of the document matching Section 3. That is, a reward is given depending on the manner of response RwdRet or RwdQA when the system presents an appropriate response. On the other hand, when the system presents an incorrect response, a penalty is given based on extraneous time, which is approximated by the total number of sentences in all turns before the appropriate information is obtained.
The value of AddSent is calculated as an expected number of additional sentences before accessing the correct response assuming the probability for success by rephrasal was p. Output by speech Overview of Bayes risk-based dialogue management. Thus, AddSent depends on variable F P. The risks of four response candidates are illustrated. The optimal response candidate is determined for p di W and pQA di W as shown in the bold line. Risk Act di Rej Conf Pres. Success rates v. Therefore, the system chooses a confirmation before presenting the entire document. Previous query: Tell me about the Silver Pavilion.
The matching score M atch W, di is then transformed into a confidence measure p di using a logistic sigmoid function. For tractable inputs, the system will learn to present documents or answers more efficiently. Thus, training with several dialogue sessions should lead to optimal decisions being made considering the current success rate of retrieval. The proposed method is also expected to adapt to changes in the data, by periodically conducting parameter updates.
This is one of the advantages of using the proposed method, as compared to the previous works Levin and Pieraccini, ; Horvitz and Paek, The training procedure can be described in four steps. This is elaborated in the following subsections. If we assume the output of Equation 2. We demonstrate that the optimal value is obtained with a small number of samples.
In a document retrieval task, since the matching score M atch W, d , which corresponds to the state space S in this task, can take any positive number, we need to train the value Q S, A for the continuous state space. We thus represent the values of responses for the current state by a function approximation system instead of a lookup table Singh et al. Williams and Young, is similar to a RL for the continuous state space.
Spoken Dialogue Systems Technology and Design - Semantic Scholar
Example of a value function. The set of 1, utterances 1, information queries and wh-questions is used in this evaluation. We trained the dialogue strategy by optimizing the parameters. The evaluation measures were the success rate and the average number of sentences for information access.
We regard a retrieval as successful if the system presented or confirmed to present the appropriate response for the utterance. The F P was set to 6 based on typical recovery patterns observed in the field trial Misu and Kawahara, Figure 8 shows the relationship between the number of steps t for learning and the success rate of information access, and Figure 9 shows the relationship between the number of steps t and the average number of expected ARP per query obtained by the step t strategy at that time A small number of ARP implies a better dialogue strategy.
We then evaluated the performance in terms of convergence speed. The BRML was converged very quickly with almost 50 samples. This convergence speed is one of the advantages of the BRML method such as when developing a new system or adapting it to changes in the tendency in the data. Of course other techniques, such as the natural gradient approach Peters and Schaal, may improve the speed, but training by RLG requires a large number of iterations, especially when dealing with a continuous state space.
One reason for this is that the RLG considers each response action as an independent one using no a priori knowledge about the dependency between responses. Success rate of information access. In contrast, if it is penalized by confirmation, the penalty is supposed to be less with rejection. Thus, the method can estimate the risk of response with a fewer number of parameters. For these reasons, we consider that the training by BRML was converged with a small number of steps.
The target of the optimization in BRML is parameters of the logistic sigmoid function that estimate posterior probability of success, and it does not depend on the values of reward and penalty. This means that the optimality of the dialogue strategy by the proposed method is guaranteed. This property is an important advantage over the other approaches that require the whole training process using the re-tuned parameters. Conclusions We have proposed an online learning method of dialogue framework to generate an optimal response based on Bayes risk.
Experimental evaluations by real user utterances demonstrated that the optimal dialogue strategy can be obtained with a small number of training samples. Although we only implemented and evaluated a simple explicit confirmation that asks the user whether the retrieved document is a correct one or not, the proposed method is expected to incorporate more various responses in document retrieval tasks, such as a clarification request and an implicit confirmation.
We used only two parameters of the matching score and bias for the logistic regression Equation 2. However, we can optimize the entire dialogue by introducing a cumulative future reward and the optimization process of WFSTs Hori et al. This method is expected to realize high precision for our task, but high recall is not expected. We thus back off inputs to the information query mode in case they are not classified as questions.
Bunsetsu is defined as a basic unit of Japanese grammar and it consists of a content word or a sequence of nouns followed by function words. We conducted the dependency structure analysis on all sentences in the knowledge base. Demonstratives e. This corresponds to a logistic regression of the success rate.
In this task, a reward or a penalty is given as a sooner reward.
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This problem corresponds to a multi-armed bandit problem with a continuous state space. These values were calculated using the manually labeled correct responses. The response with the minimum risk is selected in the Bayes risk-based strategies and the response with the minimum value is selected in the strategy using RL. References Bohus, D. Black, A. Boni, M. Natural Language Engineering, 11 4 — Chen, B. In Proceedings of Interspeech, pages — Dohsaka, K. Hori, C. Horvitz, E. User Modeling and User Adapted Interaction, 17 — Kim, D. Kim, K. Komatani, K.
User Modeling and User-Adapted Interaction, 15 1 — New Generation Computing, — Lamel, L. Speech Communication, 38 1. Lee, A. Lemon, O. Machine Learning for Spoken Dialogue Systems. Levin, E. Value-based Optimal Decision for Dialog Systems. IEEE Trans. Matsuda, M. In Proceedings of Interspeech, pages 9— Speech Communication, 48 9 — Speech Communication, 52 1 — Murata, M. Pan, Y. Peters, J. Natural Actor-Critic. Neurocomputing, 71 79 — Potamianos, A. Raux, A. In Proceedings of Interspeech. Ravichandran, D.
Reithinger, N. Rosset, S. Roy, N. Spoken Dialogue Management using Probabilistic Reasoning. Rudnicky, A. Seneff, S. Singh, S. Journal of Artificial Intelligence Research, — Sturm, J. Kudo, Y. Williams, J.
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Young, S. Zue, V. To solve problems emerging from this complexity, a technique which has attracted increasing interest during the last decades is based on the automatic generation of dialogues between the system and a user simulator, which is another system that represents human interactions with the dialogue system.
This chapter describes the main methodologies and techniques developed to create user simulators, and presents a discussion of their main characteristics and the benefits that they provide for the development, improvement and assessment of this kind of systems. Additionally, we propose a user simulation technique to test the performance of spoken dialogue systems. The technique is based on a novel approach to simulating different levels of user cooperativeness, which W.
In the experiments we have evaluated a spoken dialogue system designed for the fast food domain. The evaluation has focused on the performance of the speech recogniser, semantic analyser and dialogue manager of this system. The results show that the technique provides relevant information to obtain a solid evaluation of the system, enabling us to find problems in these modules which cannot be observed taking into account just one cooperativeness level. User modelling; Evaluation methodologies. Introduction The design of dialogue systems is a complex task that generally requires the use of expert knowledge acquired in the development of previous systems, including tests taken with users interacting with the system.
The development of these systems is usually an iterative process in which different prototypes are released and tested with real users Nielsen, The tests provide a basis for refinements of the prototype systems until eventually a system is obtained which is as perfect as possible in terms of correct functioning and user satisfaction.
However, employing user studies to support the development process is very expensive and time consuming. The use of techniques like Wizard of Oz Dow et al. For these reasons, during the last decade many research groups have been attempting to find a way to automate these processes, leading to the appearance of the first user simulators.
These simulators are automatic systems that represent human interactions with the dialogue system to be tested. Research in techniques for user modelling has a long history within the fields of natural language processing and spoken dialogue systems. Collecting large samples of interactions with real users is an expensive process in terms of time and effort.
Moreover, each time changes are made to the system it is necessary to collect more data in order to evaluate the changes. The user simulator makes it possible to generate a large number of dialogues in a very simple way. Therefore, these techniques contribute positively to the development of dialogue systems, reduce the time and effort that would be needed for their evaluation and also allow to adapt them to deal with individual user needs and preferences.
For example, in order to evaluate the consequences of the choice of a particular confirmation strategy on transaction duration or user satisfaction, simulations can be done using different strategies and the resulting data can be analyzed and compared. Another example is the introduction of errors or unpredicted answers in order to evaluate the capacity of the dialogue manager to react to unexpected situations. A second usage is to support the automatic learning of optimal dialogue strategies using reinforcement learning, given that large amounts of data are required for a systematic exploration of the dialogue state space.
Corpora of simulated data are extremely valuable for this purpose, considering the costs of collecting data from real users. In any case, it is possible that the optimal strategy may not be present in a corpus of dialogues gathered from real users, so additional simulated data may enable additional alternative choices in the state space to be explored Schatzmann et al. Working at the word level, the input to the simulator is either the words in text format or the user utterances. Words in text format allow testing the performance of the spoken language understanding SLU component of the dialogue system, and that of the dialogue manager in dealing with ill-formed sentences.
Using utterances voice samples files enables deeper checking of the robustness of the system. For example, it allows testing the performance of techniques at the ASR level to deal with noisy conditions. If the simulator works at the intention level, it receives as input abstract representations of the semantics of sentences, for example frames Eckert et al. Hence, it is not possible to check the performance of the speech recogniser or the SLU component, but only that of the dialogue manager. This strategy is useful, however, to address the problem of data sparseness and to optimise dialogue management strategies.
In this chapter we summarize the main characteristics and advantages of user simulation techniques and present a technique to enhance a rule-based user simulator previously developed by including different levels of user cooperativeness. Our user simulator has been used to improve the system by identifying problems in the performance of the speech recogniser, semantic analyser and dialogue manager. Moreover, the evaluation results provide valuable information about how to best tune the dialogue management strategies and language models for speech recognition to meet the needs of real users.
The remainder of this chapter is organised as follows. Section 2 discusses previous studies on user simulator for spoken dialogue systems. Section 3 presents our two user simulators: the initial one and an enhanced simulator which implements a fine-grained scale user cooperativeness to better evaluate spoken dialogue systems. Section 4 presents the experiments carried out employing the enhanced simulator to evaluate the performance of the Saplen dialogue system. Section 5 discusses the experimental results, the findings by employing three types of user cooperativeness, and possibilities for future work.
Finally, Section 6 presents the conclusions. Related Work The implementation of user simulators has been carried out using mainly two techniques: rule-based methods Chung, ; Komatani et al. There are also in the literature hybrid techniques which combine features of these two approaches. It is also possible to classify the different approaches with regard to the level of abstraction at which they model dialogue.
This can be at the acoustic level, the word level or the intention level. The latter is a particularly useful compressed representation of human-computer interaction. Intentions cannot be observed, but they can be described using speech-act and dialogue-act theory Searle, ; Traum, ; Bunt, For dialogue modelling, simulation at the intention level is the most convenient, since the effects of recognition and understanding errors can be modelled and the intricacies of natural language generation can be avoided Young, In this section we explain the main features of rule-based and corpus-based approaches and discuss a number of user simulators representative of each Towards Fine-Grain User-Simulation For Spoken Dialogue Systems 57 type.
The end of the section discusses issues concerning the evaluation of user simulators. The advantage of this approach is in the certainty of the reactions of the simulator, which enables the designer to have complete control over the experiments. An initial example can be found in the study presented in Araki et al.
An additional system, called coordinator, includes linguistic noise in the interaction in order to simulate speech recognition errors in the communication channel, as can be observed in Figure 1. Figure 1. Concept of system-to-system dialogue with linguistic noise Araki et al. Another technique, described in Chung, , allows two types of simulated output text and speech receiving as input simulated semantic frames Figure 2.
In the experiments carried out in the restaurant information domain, the authors generated 2, dialogues in text mode, which were particularly useful for extending the coverage of the NL parser, and to diagnose problems overlooked in the rule-based mechanisms for context tracking. In order to check the n-gram language models employed by the speech recogniser bigrams and trigrams , the authors generated 36 dialogues in speech mode. Of these dialogues, 29 were completed without errors, with the correct desired data set achieved.
SDS integrated with user simulator Chung, In earlier development cycles, these experiments were crucial to find combinations of constraints that yielded problematic responses of the system. Filisko and Seneff propose an advanced rule-based user simulator that could be configured by the developer in order to show different behaviours in the dialogues.
The authors were interested in checking error recovery mechanisms of a SDS working in the flight reservation domain.
Designing and Evaluating an Adaptive Spoken Dialogue System
The goal was to acquire out-of-vocabulary city names by means of subdialogues in which the user had to speak-and-spell city names, e. In order to make its responses as realistic as possible, the simulator combined segments of user utterances available from a speech corpus for the application domain. To combine the segments, the simulator employed a set of utterance templates.
This way, when generating a response, it chose one template, filled the existing gaps for example, departure and destination cities and produced the complete sentence Figure 3. The advantage of this approach lies in its simplicity and in that it is totally domain independent. The main disadvantage, however, is that it may be too limited to give a realistic simulated behaviour because, although user actions are dependent on the previous system action, they should also be consistent throughout the dialogue as a whole. Statistical models of user behaviour have been suggested as the solution to the lack of data when training and evaluating dialogue strategies.
Using this approach, the dialogue manager can explore the space of possible dialogue situations and learn new potentially better strategies. The most extended methodology for machine-learning of dialogue strategies consists of modeling human-computer interaction as an optimization problem using Markov Decision Process MDP and reinforcement learning methods. Eckert et al. The proposed model has the advantage of being both statistical and task-independent.
Its main weakness is that it approximates the complete history of the dialogue by only a bigram model. Both models have the drawback of considering that every user response depends only on the previous system turn. Therefore, the user simulator can change objectives continuously or repeat information previously provided. In the case of advanced dialogue systems, the possible paths through the dialogue state space are not known in advance and the specification of all possible transitions is not possible.
However, the state space is too big for an exact POMDP optimization and currently there are no methods for exhaustively searching the complete state space of a dialogue system in which the state space is emergent rather than predetermined. This issue has been addressed by constraining the state space to a manageable size and by focusing on task-oriented systems in which the goal is to elicit a finite generally fairly small set of values from the user to fill the slots in a form.
One possible way to address some of these issues is to collect and analyze vast amounts of data covering the different ways in which users interact with a system and the different choices that can be applied in dialogue management. However, controlling all these factors with real users in actual interactions would be a daunting, if not impossible task.
A more efficient method for collecting data under controlled conditions would be to simulate interactions in which the various user and system factors can be systematically manipulated. Scheffler and Young propose a graph-based model. The arcs of the network symbolize actions, and each node represents user decisions choice points. In-depth knowledge of the task and great manual effort are necessary for the specification of all possible dialogue paths. Pietquin and Beaufort combined characteristics of the models proposed in Scheffler and Young, and Levin and Pieraccini, The main objective was to reduce the manual effort necessary for the construction of the networks.
A Bayesian network was suggested for user modelling. All model parameters were hand-selected. Georgila et al. Dialogue is described as a sequence of Information States Bos et al. Two different methodologies are described to select the next user action given a history of information states. The first method uses ngrams Eckert et al.
The best results were obtained with 4-grams. The second methodology is based on the use of a linear combination of characteristics to calculate the probability of every action for a specific state. One example of this type of current status is whether or not the information needed to perform a given action has been confirmed. The simulator actions were decided considering a probability distribution learned in the training. The authors carried out experiments with the Communicator corpus dialogues concerning flights, hotels, and car reservation. Instead of training only a generic HMM model to simulate any type of dialogue, the dialogues of an initial corpus are grouped according to the different objectives.
A submodel is trained for each one of the objectives, and a bigram model is used to predict the sequence of objectives. In Schatzmann et al. The user agenda is a structure that contains the pending user dialogue acts that are needed to elicit the information specified in the goal. This model formalizes human-machine dialogues at a semantic level as a sequence of states and dialogue acts.
An EM-based algorithm is used to estimate optimal parameter values iteratively. Wang et al. The proposed simulation technique obtains the probability model via an interplay between a probabilistic user model and a dialogue system that answers queries for a restaurants information domain. The main goal was to find a way to acquire language model training material in the absence of any in-domain real user data. The dialogues that were obtained by means of the interaction between the user simulator and the dialogue system are then used to acquire adequate coverage of the possible syntactic and semantic patterns to train both the recognizer and the natural language system.
Experimental results verify that the resulting data from user simulation runs are much more refined than the original data set, both in terms of the semantic content of the sentences i. Griol et al. A labelled corpus of dialogues is used to estimate the user model, which is based on a classification methodology.
An error simulator is used to introduce errors and confidence measures from which it is possible to adapt the error simulator module to the operation of any ASR and NLU modules. A study of the evolution of the strategy followed by the dialogue manager shows how it modifies its strategy by detecting new correct answers that were not defined in the initial strategy. Figure 4. A data-driven user simulation technique for simulating user intention and utterance is introduced in Jung et al. The user intention modeling and generating method uses a linear-chain conditional random field, and a two-phase data-driven domain-specific user utterance simulation method and a linguistic knowledge-based ASR channel simulation method.
Different evaluation metrics were introduced to measure the quality of user simulation at intention and utterance. The main conclusions obtained from experimentation with a dialogue system for car navigation indicate that the user simulator was easy to set up and showed similar tendencies to real human users. One example is in Torres et al. Using this model, the dialogue manager selects the following state taking into account the last user turn and its current system state. The user simulator proposed in this work is a version of this dialogue manager, which has been modified to play the user role.
It uses the same bigram model of dialogue acts. Using this model, the user simulator selects the following user action depending only on the last system action, as in Eckert et al. Additional information rules and restrictions that depend on the user goals is included in the model to achieve the cooperation of the user and the consistency of the dialogues.
Figure 5 shows the block diagram of the dialogue system extended with the user simulator modules. Figure 5. Torres et al. A first classification divides evaluation techniques into direct evaluation methods and indirect methods Young, Direct methods evaluate the user simulator by measuring the quality of its predictions. Typically, the Recall measure has been used to take into account how many actions in the real response are predicted correctly, whereas the Precision measure has been used to consider the proportion of correct actions among all the predicted actions.
For example, Schatzmann et al. One drawback of these measures is that they consider a high penalty for the actions that are unseen in the responses on the simulator, although they could be potentially provided by a real user. Another example is Scheffler and Young, , which defines evaluation features at three dimensions: high-level features dialogue and turn lengths , dialogue style speech-act frequency; proportion of goal-directed actions, grounding, formalities, and unrecognized actions; proportion of information provided, reprovided, requested and rerequested , and dialogue efficiency goal completion rates and times.
The simulation presented in Schatzmann et al. In Georgila et al. It determines whether the simulated dialogues contain sequences of actions that are similar to those contained in the real dialogues. The aim of the work described in Ai and Litman, is to extend the previous work to evaluate the extent to which state-of-the-art user simulators can mimic human user behaviours and how well they can replace human users in a variety of tasks. Schatzmann et al. These evaluation measures have proven to be sufficient to discern simulated from real dialogues Griol et al.
Judges were asked to read the transcripts of the dialogues between the computer tutoring system and the simulators and to rate the dialogues on a 5-point scale from different perspectives. However, these ratings provide consistent ranking on the quality of the real and the user simulator models. They concluded that this ranking model can be used to quickly assess the quality of a new simulation model without manual efforts by ranking the new model against the traditional ones. The main objective of indirect methods of evaluation is to measure the utility of the user simulator within the framework of the operation of the complete system.
These methods try to evaluate the operation of the dialogue strategy learned by means of the simulator. This evaluation is usually carried out by verifying the operation of the new strategy through a new interaction with the simulator. Then, the initial strategy is compared with the learned one using the simulator. The main problem with this evaluation resides in the dependence of the acquired corpus on the user model.
The results indicate that the choice of the user model has a significant impact on the learned strategy. The results also demonstrate that a strategy learned with a high-quality user model generalizes well to other types of user models. Lemon and Liu extend this work by evaluating only one type of stochastic user simulation but with different types of users and under different environmental conditions. This study concludes that dialogue policies trained in high-noise conditions perform significantly better than those trained for low-noise conditions.
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Ai et al. Three different user simulators, which were trained from a real corpus and simulate the word level, were evaluated using a speech-enabled Intelligent Tutoring System that helps students understand qualitative physics questions. The first model, called Probabilistic Model PM , is meant to capture realistic student behavior in a probabilistic way.
The second model, called Total Random Model TRM ignores what the current question is or what feedback is given and randomly picks one utterance from all the utterances in the entire candidate answer set. The third model, called Restricted Random Model RRM differs from the PM in that given a certain tutor question and a tutor feedback, it chooses to give certain, uncertain, neutral, or mixed answer with equal probability. Our User Simulators This section of the chapter focuses on our initial user simulator and its application to evaluate the performance of the Saplen dialogue system.
It addresses improvements made in the simulator to create an enhanced version. Your alerts can be managed through your account. This title is in stock with our Australian supplier and arrives at our Sydney warehouse within weeks of your order. Once received into our warehouse we will despatch it to you with a Shipping Notification which includes online tracking.
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Spoken Dialogue Systems Technology and Design
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