Search

Abstract Web-scale discovery service is one of the primary trends in academic libraries. In this research, using referral data, we explore the main referring domains of DOIs, and evaluate the impact of web-scale discovery services on scholarly content seeking. The results show that web-scale discovery services play an important role in directing users to scholarly contents. ProQuest is found to be ranked the first among all the major referring domains. In addition, the time series analysis shows a significant growing trend of ProQuest as a referring service from 2011 to 2016. The monthly trends demonstrate seasonal fluctuations and daily periodicity of referral data. Specifically, there are two low ebbs throughout a year, corresponding to the Summer and Winter vacations. The lowest point in a week is found to be on Saturdays, while the peak normally occurs on Mondays or Tuesdays. [ABSTRACT FROM AUTHOR]

Abstractr We propose Quantized Dialog , a novel approach for the development of conversational systems. The methodology relies on the semantic quantization and clustering of the dialog utterances in order to reduce the dialog interaction space, making prediction of the next utterance more tractable. The effectiveness of this method is showcased using the goal-oriented dataset of the sixth Dialog System Technology Challenge (DSTC6). We compare the performance of Quantized Dialog based on an n-gram language model for next-utterance prediction against other models that employ popular deep-learning architectures, such as multi-layer neural network classifiers, memory networks, long short-term memory recurrent neural networks and convolutional neural networks. The experimental results demonstrate the promising potential of the new quantized approach in goal-oriented dialog prediction. Highlights • Quantized Dialog relies on semantic quantization and clustering of dialog utterances. • Once the proper clusters are obtained, the dialog policy can be learned efficiently. • A language model is obtained by learning transitions probabilities between clusters. • All five language models tested show very good performance (accuracy close to 100). • Quantized Dialog appealingly simple and promising option for conversational systems. [ABSTRACT FROM AUTHOR]

Highlights • An end-to-end goal-oriented dialog learning system is proposed. • Learning is conducted using a Bi-LSTM memory network model. • Performance increased with the use of metadata. • The Bi-LSTM memory network improves on the original and dynamic memory networks. Abstract We develop a model to satisfy the requirements of Dialog System Technology Challenge 6 (DSTC6) Track 1: building an end-to-end dialog systems for goal-oriented applications. This task involves learning a dialog policy from transactional dialogs in a given domain. Automatic system responses are generated using given task-oriented dialog data (http://workshop.colips.org/dstc6/index.html). As this task has a similar structure to a question answering task (Weston et al., 2015), we employ the MemN2N architecture (Sukhbaatar et al., 2015), which outperforms models based on recurrent neural networks or long short-term memory (LSTM). However, two problems arise when applying this model to the DSTC6 task. First, we encounter an out-of-vocabulary problem, which we resolve by categorizing the metadata types of words that exist in the knowledge base; the metadata is similar to the named entity. Second, the original memory network model has a weak ability to reflect sufficient temporal information, because it only uses sentence-level embeddings. Therefore, we add bidirectional LSTM (Bi-LSTM) at the beginning of the model to better reflect temporal information. The experimental results demonstrate that our model reflects temporal features well. Furthermore, our model achieves state-of-the-art performance among the memory networks, and is comparable to hybrid code networks (Ham et al., 2017) and hierarchical LSTM model (Bai et al., 2017) which is not an end-to-end architecture. [ABSTRACT FROM AUTHOR]

Highlights • The proposed system extends some of modules to be learned from data. • The proposed system is adaptable to changes in the knowledge base. • Extensions to hybrid code networks achieve the perfect accuracy in all DSTC6 test datasets. • The source code is available for download from github. Abstract Goal-oriented dialog systems require a different approach from chit-chat conversational systems in that they should perform various subtasks as well as continue the conversation itself. Since these systems typically interact with an external knowledge base that changes over time, it is desirable to incorporate domain knowledge to deal with such changes, yet with minimum human effort. This paper presents an extended version of the Hybrid Code Network (HCN) developed for the Facebook AI research (FAIR) dialog dataset used in the Sixth Dialog System Technology Challenge (DSTC6). Compared to the original HCN, the system was more adaptable to changes in the knowledge base due to the modules that are extended to be learned from data. Using the proposed learning scheme with fairly elementary domain-specific rules, the proposed model achieved 100% accuracy in all test datasets. [ABSTRACT FROM AUTHOR]

In this paper, we address issues in situated language understanding in a moving car. More specifically, we propose a reference resolution method to identify user queries about specific target objects in their surroundings. We investigate methods of predicting which target object is likely to be queried given a visual scene and what kind of linguistic cues users naturally provide to describe a given target object in a situated environment. We propose methods to incorporate the visual saliency of the visual scene as a prior. Crowdsourced statistics of how people describe an object are also used as a prior. We have collected situated utterances from drivers using our research system, which was embedded in a real vehicle. We demonstrate that the proposed algorithms improve target identification rate by 15.1% absolute over the baseline method that does not use visual saliency-based prior and depends on public database with a limited number of category information. [ABSTRACT FROM AUTHOR]

During dialog, references are presented, accepted, and potentially reused (depending on their accessibility in memory). Two experiments were conducted to examine reuse in a naturalistic setting (a walk in a familiar environment). In Experiment 1, where the participants interacted face to face, self-presented references and references accepted through verbatim repetition were reused more. Such biases persisted after the end of the interaction. In Experiment 2, where the participants interacted over the phone, reference reuse mainly depended on whether the participant could see the landmarks being referred to, although this bias seemed to be only transient. Consistent with the memory-based approach to dialog, these results shed light on how differences in accessibility in memory (due to how these references were initially added to the common ground or the media used) affect the unfolding of the interaction. [ABSTRACT FROM AUTHOR]

Context: The role of paternal alcohol consumption on fetal and infant health outcomes, and on social facilitation of maternal alcohol consumption during pregnancy, has not been well established. This review identifies the range of impacts of paternal preconception alcohol consumption and alcohol consumption during partner's pregnancy, on maternal consumption, and fetal and infant health outcomes.Evidence Acquisition: The review accessed articles from the following databases: Scopus, Science Direct, Wiley Online, MEDLINE, ProQuest Central, PsycINFO, and Web of Science. The review included medium- and large-scale studies that provided separate paternal alcohol results, had a non-respondent rate ≤20%, an attrition rate ≤10% per year of data collection up to 30%, and were published between 1990 and 2014. The review included both randomly and non-randomly selected studies, and both case-control and non-case-control studies with notation on risk of bias.Evidence Synthesis: Independent extraction and assessment of articles by two authors was conducted using predefined data fields, including study quality indicators, during 2015. Studies included in the review (11 studies, N=41,062) provide evidence that paternal alcohol consumption during preconception or during pregnancy has an impact on maternal health and alcohol consumption during pregnancy, fetal outcomes, and infant health outcomes.Conclusions: Attention to paternal preconception health care related to alcohol consumption is an important future focus in policies dealing with reproductive, prenatal, fetal, and infant health. [ABSTRACT FROM AUTHOR]

The article offers information on the summer school "Der Zweite Weltkrieg aus transnationaler Perspektive: Neue Forschungsansätze im Dialog" that was held in Berlin, Germany on September 7-12, 2015. The aim of the summer school is to provide opportunities for young scientists from the field of science of history and related disciplines and discussed the new approaches and innovations in the subject of the Second World War.

This study examines worldwide usage of over 600,000 e-books from Ebook Library (EBL) and ebrary. Using multiple modes of analysis, the study shows that there are variations in usage by geographic region as well as by subject. The study examines usage in relation to availability of titles, different types of usage per session, usage of the top ten percent of titles, and intensive and extensive use. These patterns can be used for benchmarking and as a model for local e-book studies. [ABSTRACT FROM AUTHOR]

Search in audio archives is a challenging problem. Using prosodic information to help find relevant content has been proposed as a complement to word-based retrieval, but its utility has been an open question. We propose a new way to use prosodic information in search, based on a vector-space model, where each point in time maps to a point in a vector space whose dimensions are derived from numerous prosodic features of the local context. Point pairs that are close in this vector space are frequently similar, not only in terms of the dialog activities, but also in topic. Using proximity in this space as an indicator of similarity, we built support for a query-by-example function. Searchers were happy to use this function, and it provided value on a large testset. Prosody-based retrieval did not perform as well as word-based retrieval, but the two sources of information were often non-redundant and in combination they sometimes performed better than either separately. [ABSTRACT FROM AUTHOR]

Abstract: How is it that, given our diverse sensors that are moving at any moment, we get to the idea that there is a more or less permanent world around us that contains objects and living beings and that is endowed with spatial and temporal properties? This question is difficult, it is “the child’s construction of reality” issue. Given the complexity of the underlying phenomena, it requires a dialog between engineering sciences and life sciences to be solved. In this paper, we introduce the contributions to this dialog that have been collected in this special issue. [Copyright &y& Elsevier]

In this paper, a multimodal user-emotion detection system for social robots is presented. This system is intended to be used during human-robot interaction, and it is integrated as part of the overall interaction system of the robot: the Robotics Dialog System (RDS). Two modes are used to detect emotions: the voice and face expression analysis. In order to analyze the voice of the user, a new component has been developed: Gender and Emotion Voice Analysis (GEVA), which is written using the Chuck language. For emotion detection in facial expressions, the system, Gender and Emotion Facial Analysis (GEFA), has been also developed. This last system integrates two third-party solutions: Sophisticated High-speed Object Recognition Engine (SHORE) and Computer Expression Recognition Toolbox (CERT). Once these new components (GEVA and GEFA) give their results, a decision rule is applied in order to combine the information given by both of them. The result of this rule, the detected emotion, is integrated into the dialog system through communicative acts. Hence, each communicative act gives, among other things, the detected emotion of the user to the RDS so it can adapt its strategy in order to get a greater satisfaction degree during the human-robot dialog. Each of the new components, GEVA and GEFA, can also be used individually. Moreover, they are integrated with the robotic control platform ROS (Robot Operating System). Several experiments with real users were performed to determine the accuracy of each component and to set the final decision rule. The results obtained from applying this decision rule in these experiments show a high success rate in automatic user emotion recognition, improving the results given by the two information channels (audio and visual) separately. [ABSTRACT FROM AUTHOR]

Highlights: [•] It is possible to model affect using a corpus that was collected from an evaluation that was originally meant for non-emotion related assessment. [•] Conversational features could be used as a single source to model user affect in a limited-task domestic agent. [•] Affect could be inferred by directly predicting user satisfaction (modelling satisfaction). [•] User bias is empirically detected within a laboratory-led evaluation in this paper. [•] The effects of Contextual Turns on user satisfaction depend on user skill. [Copyright &y& Elsevier]

Recently, Shen et al. (Int. J. Theor. Phys. doi:, ) proposed a quantum dialogue with authentication protocol based on Bell states. This study points out that Shen et al.'s protocol suffers from a man-in-the-middle attack. By manipulating the photons and classical information transmitted between two communicants, an attacker can not only pass the mutual identity authentication, but also obtain their secret messages and new authentication keys without being detected. [ABSTRACT FROM AUTHOR]

A quantum dialogue protocol is proposed based on triple particle W states. Each particle is transferred only once in the entire communication, which reduces the risk of eavesdropping and improves the security of the protocol. The protocol can resist various attacks. The efficiency of this protocol can be up to 80 % without information leakage. [ABSTRACT FROM AUTHOR]

Abstract: Automatically detecting human social intentions and attitudes from spoken conversation is an important task for speech processing and social computing. We describe a system for detecting interpersonal stance: whether a speaker is flirtatious, friendly, awkward, or assertive. We make use of a new spoken corpus of over 1000 4-min speed-dates. Participants rated themselves and their interlocutors for these interpersonal stances, allowing us to build detectors for style both as interpreted by the speaker and as perceived by the hearer. We use lexical, prosodic, and dialog features in an SVM classifier to detect very clear styles (the strongest 10% in each stance) with up to 75% accuracy on previously seen speakers (50% baseline) and up to 59% accuracy on new speakers (48% baseline). A feature analysis suggests that flirtation is marked by joint focus on the woman as a target of the conversation, awkwardness by decreased speaker involvement, and friendliness by a conversational style including other-directed laughter and appreciations. Our work has implications for our understanding of interpersonal stance, their linguistic expression, and their automatic extraction. [Copyright &y& Elsevier]

Abstract: The purpose of this research was to gain insights into SMS communication among French-speaking adolescents. We analyzed the effects of the writers’ characteristics (age, gender, and SMS-messaging experience) on message length (number of characters and number of words), dialogue structure (with or without an opening and a closing), and message function (informative vs. relational). The SMS messages were produced in a real-world situation. We found differences across writers’ characteristics for all the dependant variables. The commonly reported distinctions between girls and boys were mitigated. Moreover, for dialogical structure, the messages differed from those found in traditional oral and written interactions since 73% of them did not have the conventional opening–message–closing format (the opening and/or the closing was missing). The results are discussed in terms of the specific characteristics that define the SMS register, and potentially relevant approaches to be taken in future research are addressed. [Copyright &y& Elsevier]

The elderly population is the most heterogeneous group. In advanced years a lot of people are still fit enough for quality, creative and independent living. As there are differences in the ability of elderly people there are differences in their needs. On the other hand, it is not negligible proportion of those who are partially or totally dependent on help, by meeting the same needs. This paper will present the results of the survey, which was used to determine which institutions are dealing with elderly population and what is the role of gender in establishing intergenerational dialog in private and in public spheres. We will demonstrate the age and educational structure of employees working with elderly population with special stress on competences that are needed to establish an effective intergenerational dialogue, which is the foundation of intergenerational coexistence. [ABSTRACT FROM AUTHOR]

Abstract: The present study set out to identify and examine dialogic educational interactions in Finnish pre-school classrooms. Video recordings of five observed pre-school classrooms that had shown a high or moderate quality of instructional support in literacy, maths and science studies were transcribed for micro-scale qualitative content analysis. Three patterns of teacher–child interaction emerged: first, a pattern characterised by the teacher making it possible for the children to demonstrate their knowledge and competence; second, a pattern characterised by the teacher supporting the children''s participation and diverse contributions; and third, a pattern characterised by the teacher allowing dialogical space for a child-initiated sharing of ideas. The findings provide guidelines for professional development programmes seeking to increase teachers’ awareness of ways to support children''s active participation. [Copyright &y& Elsevier]

This paper presents two cognitive models that simulate the production of referring expressions in the iMAP task-a task-oriented dialog. One general model is based on Dale and Reiter's (1995) incremental algorithm, and the other is a simple template model that has a higher correlation with the data but is specifically geared toward the properties of the iMAP task. The property of the iMAP task environment that is modeled here is that the color feature is unreliable for identifying referents while other features are reliable. The low computational cost of the incremental algorithm for generating referring expressions makes it an interesting starting point for a cognitive model. However, its explanatory power is limited, because it generates uniquely distinguishing referring expressions and because it considers features for inclusion in the referring expression in a fixed order. The first model extends the original incremental algorithm by an ability to adapt to feedback of whether a referring expression was used successfully, but it seems to overpredict the frequency with which distinguishing expressions are made and underpredict the frequency of overspecified referring expressions. The second model produces features for referring expressions purely based on its current estimate of a feature's utility. Both models predict the observed human behavior of decreasing use of color terms and increasing use of useful feature terms. [ABSTRACT FROM AUTHOR]

Abstract: Current development platforms for designing spoken dialog services feature different kinds of strategies to help designers build, test, and deploy their applications. In general, these platforms are made up of several assistants that handle the different design stages (e.g. definition of the dialog flow, prompt and grammar definition, database connection, or to debug and test the running of the application). In spite of all the advances in this area, in general the process of designing spoken-based dialog services is a time consuming task that needs to be accelerated. In this paper we describe a complete development platform that reduces the design time by using different types of acceleration strategies based on using information from the data model structure and database contents, as well as cumulative information obtained throughout the successive steps in the design. Thanks to these accelerations, the interaction with the platform is simplified and the design is reduced, in most cases, to simple confirmations to the “proposals” that the platform automatically provides at each stage. Different kinds of proposals are available to complete the application flow such as the possibility of selecting which information slots should be requested to the user together, predefined templates for common dialogs, the most probable actions that make up each state defined in the flow, different solutions to solve specific speech-modality problems such as the presentation of the lists of retrieved results after querying the backend database. The platform also includes accelerations for creating speech grammars and prompts, and the SQL queries for accessing the database at runtime. Finally, we will describe the setup and results obtained in a simultaneous summative, subjective and objective evaluations with different designers used to test the usability of the proposed accelerations as well as their contribution to reducing the design time and interaction. [Copyright &y& Elsevier]

Abstract: This paper describes a comprehensive usability evaluation of an automated telephone banking system which employs text-to-speech (TTS) synthesis in offering additional detail on customers’ account transactions. The paper describes a series of four experiments in which TTS was employed to offer an extra level of detail to recent transactions listings within an established banking service which otherwise uses recorded speech from a professional recording artist. Results from the experiments show that participants welcome the added value of TTS in being able to provide additional detail on their account transactions, but that TTS should be used minimally in the service. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a novel user intention simulation method which is data-driven but can integrate diverse user discourse knowledge to simulate various types of user behaviors. A method of data-driven user intention modeling based on logistic regression is introduced in the Markov logic framework. Human dialog knowledge is designed into two layers, domain and discourse knowledge, and integrated with the data-driven model in generation time. Three types of user knowledge, i.e., cooperative, corrective and self-directing, are designed and integrated to generate behaviors of corresponding user-types. In experiments to investigate the patterns of simulated users, the approach successfully generated cooperative, corrective and self-directing user intention patterns. [ABSTRACT FROM AUTHOR]

Abstract: Multimodal conversational spoken dialogues using physical and virtual agents provide a potential interface to motivate and support users in the domain of health and fitness. This paper describes how such multimodal conversational Companions can be implemented to support their owners in various pervasive and mobile settings. We present concrete system architectures, virtual, physical and mobile multimodal interfaces, and interaction management techniques for such Companions. In particular how knowledge representation and separation of low-level interaction modelling from high-level reasoning at the domain level makes it possible to implement distributed, but still coherent, interaction with Companions. The distribution is enabled by using a dialogue plan to communicate information from domain level planner to dialogue management and from there to a separate mobile interface. The model enables each part of the system to handle the same information from its own perspective without containing overlapping logic, and makes it possible to separate task-specific and conversational dialogue management from each other. In addition to technical descriptions, results from the first evaluations of the Companions interfaces are presented. [ABSTRACT FROM AUTHOR]

Abstract: This paper studies the multifunctionality of dialogue utterances, i.e. the phenomenon that utterances in dialogue often have more than one communicative function. It is argued that this phenomenon can be explained by analyzing the participation in dialogue as involving the performance of several types of activity in parallel, relating to different dimensions of communication. The multifunctionality of dialogue utterances is studied by (1) redefining the notion of ‘utterance’ in a rigorous manner (calling the revised notion ‘functional segment’), and (2) empirically investigating the multifunctionality of functional segments in a corpus of dialogues, annotated with a rich, multidimensional annotation schema. It is shown that, when communicative functions are assigned to functional segments, thereby eliminating every form of segmentation-related multifunctionality, an average multifunctionality is found between 1.8 and 3.6, depending on what is considered to count as a segment''s communicative function. Moreover, a good understanding of the nature of the relations among the various multiple functions that a segment may have, and of the relations between functional segments and other units in dialogue segmentation, opens the way for defining a multidimensional computational update semantics for dialogue interpretation. [Copyright &y& Elsevier]

Abstract: This paper argues that the problems of dialogue management (DM) and Natural Language Generation (NLG) in dialogue systems are closely related and can be fruitfully treated statistically, in a joint optimisation framework such as that provided by Reinforcement Learning (RL). We first review recent results and methods in automatic learning of dialogue management strategies for spoken and multimodal dialogue systems, and then show how these techniques can also be used for the related problem of Natural Language Generation. This approach promises a number of theoretical and practical benefits such as fine-grained adaptation, generalisation, and automatic (global) optimisation, and we compare it to related work in statistical/trainable NLG. A demonstration of the proposed approach is then developed, showing combined DM and NLG policy learning for adaptive information presentation decisions. A joint DM and NLG policy learned in the framework shows a statistically significant 27% relative increase in reward over a baseline policy, which is learned in the same way only without the joint optimisation. We thereby show that that NLG problems can be approached statistically, in combination with dialogue management decisions, and we show how to jointly optimise NLG and DM using Reinforcement Learning. [ABSTRACT FROM AUTHOR]

Abstract: A common task for spoken dialog systems (SDS) is to help users select a suitable option (e.g., flight, hotel, and restaurant) from the set of options available. As the number of options increases, the system must have strategies for generating summaries that enable the user to browse the option space efficiently and successfully. In the user-model based summarize and refine approach (UMSR, Demberg and Moore, 2006), options are clustered to maximize utility with respect to a user model, and linguistic devices such as discourse cues and adverbials are used to highlight the trade-offs among the presented items. In a Wizard-of-Oz experiment, we show that the UMSR approach leads to improvements in task success, efficiency, and user satisfaction compared to an approach that clusters the available options to maximize coverage of the domain (Polifroni et al., 2003). In both a laboratory experiment and a web-based experimental paradigm employing the Amazon Mechanical Turk platform, we show that the discourse cues in UMSR summaries help users compare different options and choose between options, even though they do not improve verbatim recall. This effect was observed for both written and spoken stimuli. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes an initial prototype of the Companions project (www.companions-project.org): the Senior Companion (SC), designed to be a platform to display novel approaches to: [(1)] The use of Information Extraction (IE) techniques to extract the content of incoming dialogue utterances after an ASR phase. [(2)] The conversion of the input to RDF form to allow the generation of new facts from existing ones, under the control of a Dialogue Manager (DM), that also has access to stored knowledge and knowledge accessed in real time from the web, all in RDF form. [(3)] A DM expressed as a stack and network virtual machine that models mixed initiative in dialogue control. [(4)] A tuned dialogue act detector based on corpus evidence. The prototype platform was evaluated, and we describe this; it is also designed to support more extensive forms of emotion detection carried by both speech and lexical content, as well as extended forms of machine learning. We describe preliminary studies and results for these, in particular a novel approach to enabling reinforcement learning for open dialogue systems through the detection of emotion in the speech signal and its deployment as a form of a learned DM, at a higher level than the DM virtual machine and able to direct the SC''s responses to a more emotionally appropriate part of its repertoire. [Copyright &y& Elsevier]

Abstract: We describe the design and evaluation of two different dynamic student uncertainty adaptations in wizarded versions of a spoken dialogue tutoring system. The two adaptive systems adapt to each student turn based on its uncertainty, after an unseen human “wizard” performs speech recognition and natural language understanding and annotates the turn for uncertainty. The design of our two uncertainty adaptations is based on a hypothesis in the literature that uncertainty is an “opportunity to learn”; both adaptations use additional substantive content to respond to uncertain turns, but the two adaptations vary in the complexity of these responses. The evaluation of our two uncertainty adaptations represents one of the first controlled experiments to investigate whether substantive dynamic responses to student affect can significantly improve performance in computer tutors. To our knowledge we are the first study to show that dynamically responding to uncertainty can significantly improve learning during computer tutoring. We also highlight our ongoing evaluation of our uncertainty-adaptive systems with respect to other important performance metrics, and we discuss how our corpus can be used by the wider computer speech and language community as a linguistic resource supporting further research on effective affect-adaptive spoken dialogue systems in general. [Copyright &y& Elsevier]

Abstract: The automatic recognition of user’s communicative style within a spoken dialog system framework, including the affective aspects, has received increased attention in the past few years. For dialog systems, it is important to know not only what was said but also how something was communicated, so that the system can engage the user in a richer and more natural interaction. This paper addresses the problem of automatically detecting “frustration”, “politeness”, and “neutral” attitudes from a child’s speech communication cues, elicited in spontaneous dialog interactions with computer characters. Several information sources such as acoustic, lexical, and contextual features, as well as, their combinations are used for this purpose. The study is based on a Wizard-of-Oz dialog corpus of 103 children, 7–14 years of age, playing a voice activated computer game. Three-way classification experiments, as well as, pairwise classification between polite vs. others and frustrated vs. others were performed. Experimental results show that lexical information has more discriminative power than acoustic and contextual cues for detection of politeness, whereas context and acoustic features perform best for frustration detection. Furthermore, the fusion of acoustic, lexical and contextual information provided significantly better classification results. Results also showed that classification performance varies with age and gender. Specifically, for the “politeness” detection task, higher classification accuracy was achieved for females and 10–11 years-olds, compared to males and other age groups, respectively. [Copyright &y& Elsevier]

Dialog act (DA) classification is useful to understand the intentions of a human speaker. An effective classification of DA can be exploited for realistic implementation of expert systems. In this work, we investigate DA classification using both acoustic and discourse information for HCRC MapTask data. We extract several different acoustic features and exploit these features using a Hidden Markov Model (HMM) network to classify acoustic information. For discourse feature extraction, we propose a novel parts-of-speech (POS) tagging technique that effectively reduces the dimensionality of discourse features. To classify discourse information, we exploit two classifiers such as a HMM and Support Vector Machine (SVM). We further obtain classifier fusion between HMM and SVM to improve discourse classification. Finally, we perform an efficient decision-level classifier fusion for both acoustic and discourse information to classify 12 different DAs in MapTask data. We obtain 65.2% and 55.4% DA classification rates using acoustic and discourse information, respectively. Furthermore, we obtain combined accuracy of 68.6% for DA classification using both acoustic and discourse information. These accuracy rates of DA classification are either comparable or better than previously reported results for the same data set. For average precision and recall, we obtain accuracy rates of 74.89% and 69.83%, respectively. Therefore, we obtain much better precision and recall rates for most of the classified DAs when compared to existing works on the same HCRC MapTask data set. [ABSTRACT FROM AUTHOR]

Abstract: When conveying information about spatial situations and goals, speakers adapt flexibly to their addressee in order to reach the communicative goal efficiently and effortlessly. Our aim is to equip a dialogue system with the abilities required for such a natural, adaptive dialogue. In this paper we investigate the strategies people use to convey route information in relation to a map by presenting two parallel studies involving human–human and human–computer interaction. We compare the instructions given to a human interaction partner with those given to a dialogue system which reacts by basic verbal responses and dynamic visualization of the route in the map. The language produced by human route givers is analyzed with respect to a range of communicative as well as cognitively crucial features, particularly perspective choice and references to locations across levels of granularity. Results reveal that speakers produce systematically different instructions with respect to these features, depending on the nature of the interaction partner, human or dialogue system. Our further analysis of clarification and reference resolution strategies produced by human route followers provides insights into dialogue strategies that future systems should be equipped with. [Copyright &y& Elsevier]

A quantum dialogue protocol is proposed and proved to be secure. In our scheme, single photons are used as a quantum channel, the security of our scheme is guaranteed by BB84 QKD and the fact that controlled-not operation ensures the control qubit is unchanged and the target qubit flips only if the control qubit is [image omitted]. By using controlled-not operations, two legitimate communicators can share a private initial state and realize quantum dialogue securely. Compared with previous quantum dialogue protocols, the good merit of this work is that the insecurity, i.e. 'information leakage' is eliminated. Comparing this dialogue protocol with the recent work in another paper [Shi, G.F.; Xi, X.Q.; Hu, M.L.; Yue, R.H. Opt. Commun. 2010, 283, 1984] where two adjacent single photons must be of the same quantum state, this scheme is experimentally simple. [ABSTRACT FROM AUTHOR]

Abstract: Spoken dialog systems have difficulty selecting which action to take in a given situation because recognition and understanding errors are prevalent due to noise and unexpected inputs. To solve this problem, this paper presents a hybrid approach to improving robustness of the dialog manager by using agenda-based and example-based dialog modeling. This approach can exploit n-best hypotheses to determine the current dialog state in the dialog manager and keep track of the dialog state using a discourse interpretation algorithm based on an agenda graph and a focus stack. Given the agenda graph and multiple recognition hypotheses, the system can predict the next action to maximize multi-level score functions and trigger error recovery strategies to handle exceptional cases due to misunderstandings or unexpected focus shifts. The proposed method was tested by developing a spoken dialog system for a building guidance domain in an intelligent service robot. This system was then evaluated by simulated and real users. The experimental results show that our approach can effectively develop robust dialog management for spoken dialog systems. [Copyright &y& Elsevier]

Software localization is one of important tasks to insure a successful computer user experience. The paper discusses how localization of the software dialog text can be accelerated and how to raise the quality of software product localization. We also discuss the main features and common structure of localizable software resources, their formats and preparation for localization. As a result, we suggest applying a modified formalism of attribute grammars to describe localizable resources, taking the graphical user interface as a basic grammar structure, localizable strings and their parts as terminal symbols, and using the attributes to add important metainformation and context to the resources. The main principles of creation of such attribute grammars are presented. [ABSTRACT FROM AUTHOR]

Abstract: This paper explains how Partially Observable Markov Decision Processes (POMDPs) can provide a principled mathematical framework for modelling the inherent uncertainty in spoken dialogue systems. It briefly summarises the basic mathematics and explains why exact optimisation is intractable. It then describes in some detail a form of approximation called the Hidden Information State model which does scale and which can be used to build practical systems. A prototype HIS system for the tourist information domain is evaluated and compared with a baseline MDP system using both user simulations and a live user trial. The results give strong support to the central contention that the POMDP-based framework is both a tractable and powerful approach to building more robust spoken dialogue systems. [Copyright &y& Elsevier]

This paper analyzes and compares the data gathered from two previously conducted Artificial Linguistic Internet Chat Entity (ALICE) chatterbot studies that were focused on response accuracy and user satisfaction measures for six chatterbots. These chatterbots were further loaded with varying degrees of conversational, telecommunications, and terrorism knowledge. From our prior experiments using 347 participants, we obtained 33446 human/chatterbot interactions. It was found that asking the ALICE chatterbots "are" and "where" questions resulted in higher response satisfaction levels, as compared to other interrogative-style inputs because of their acceptability to vague, binary, or clichéd chatterbot responses. We also found a relationship between the length of a query and the users perceived satisfaction of the chatterbot response, where shorter queries led to more satisfying responses. [ABSTRACT FROM AUTHOR]

Abstract: Prosody is an important cue for identifying dialog acts. In this paper, we show that modeling the sequence of acoustic–prosodic values as -gram features with a maximum entropy model for dialog act (DA) tagging can perform better than conventional approaches that use coarse representation of the prosodic contour through summative statistics of the prosodic contour. The proposed scheme for exploiting prosody results in an absolute improvement of 8.7% over the use of most other widely used representations of acoustic correlates of prosody. The proposed scheme is discriminative and exploits context in the form of lexical, syntactic and prosodic cues from preceding discourse segments. Such a decoding scheme facilitates online DA tagging and offers robustness in the decoding process, unlike greedy decoding schemes that can potentially propagate errors. Our approach is different from traditional DA systems that use the entire conversation for offline dialog act decoding with the aid of a discourse model. In contrast, we use only static features and approximate the previous dialog act tags in terms of lexical, syntactic and prosodic information extracted from previous utterances. Experiments on the Switchboard-DAMSL corpus, using only lexical, syntactic and prosodic cues from three previous utterances, yield a DA tagging accuracy of 72% compared to the best case scenario with accurate knowledge of previous DA tags (oracle), which results in 74% accuracy. [Copyright &y& Elsevier]

Abstract: The Gaussian mixture model – Universal background model (GMM–UBM) system is one of the predominant approaches for text-independent speaker verification, because both the target speaker model and the impostor model (UBM) have generalization ability to handle “unseen” acoustic patterns. However, since GMM–UBM uses a common anti-model, namely UBM, for all target speakers, it tends to be weak in rejecting impostors’ voices that are similar to the target speaker’s voice. To overcome this limitation, we propose a discriminative feedback adaptation (DFA) framework that reinforces the discriminability between the target speaker model and the anti-model, while preserving the generalization ability of the GMM–UBM approach. This is achieved by adapting the UBM to a target speaker dependent anti-model based on a minimum verification squared-error criterion, rather than estimating the model from scratch by applying the conventional discriminative training schemes. The results of experiments conducted on the NIST2001-SRE database show that DFA substantially improves the performance of the conventional GMM–UBM approach. [Copyright &y& Elsevier]

Abstract: This paper describes in detail a novel approach to the reordering challenge in statistical machine translation (SMT). This Ngram-based reordering (NbR) approach uses the powerful techniques of SMT systems to generate a weighted reordering graph. Thus, statistical criteria reordering constraints are supplied to an SMT system, and this allows an extension to the SMT decoding search. The NbR approach is capable of generalizing reorderings that have been learned during training, through the use of word classes instead of words themselves. Improvement in translation performance is demonstrated with the EPPS task (Spanish and German to English) and the BTEC task (Arabic to English). [Copyright &y& Elsevier]

Abstract: In this paper, we describe RavenClaw, a plan-based, task-independent dialog management framework. RavenClaw isolates the domain-specific aspects of the dialog control logic from domain-independent conversational skills, and in the process facilitates rapid development of mixed-initiative systems operating in complex, task-oriented domains. System developers can focus exclusively on describing the dialog task control logic, while a large number of domain-independent conversational skills such as error handling, timing and turn-taking are transparently supported and enforced by the RavenClaw dialog engine. To date, RavenClaw has been used to construct and deploy a large number of systems, spanning different domains and interaction styles, such as information access, guidance through procedures, command-and-control, medical diagnosis, etc. The framework has easily adapted to all of these domains, indicating a high degree of versatility and scalability. [Copyright &y& Elsevier]

Abstract: This paper presents empirical results of an analysis on the role of prosody in the recognition of dialogue acts and utterance mood in a practical dialogue corpus in Mexican Spanish. The work is configured as a series of machine-learning experimental conditions in which models are created by using intonational and other data as predictors and dialogue act tagging data as targets. We show that utterance mood can be predicted from intonational information, and that this mood information can then be used to recognize the dialogue act. [Copyright &y& Elsevier]

This paper proposes a generic dialog modeling framework for a multi-domain dialog system to simultaneously manage goal-oriented and chat dialogs for both information access and entertainment. We developed a dialog modeling technique using an example-based approach to implement multiple applications such as car navigation, weather information, TV program guidance, and chatbot. Example-based dialog modeling (EBDM) is a simple and effective method for prototyping and deploying of various dialog systems. This paper also introduces the system architecture of multi-domain dialog systems using the EBDM framework and the domain spotting technique. In our experiments, we evaluate our system using both simulated and real users. We expect that our approach can support flexible management of multi-domain dialogs on the same framework. [Copyright &y& Elsevier]

To generate correct reactions, a dialogue system should identify domain actions indicated by users' utterances because speaker intentions can be captured by the domain actions. In this paper, we propose a domain action classification model to determine speech acts (general intentions) and concept sequences (semantic focuses) at the same time in a schedule management domain. To avoid biased learning problems, the proposed model uses low-level linguistic features and filters out uninformative features using statistic. Then, the proposed model determines domain actions using a maximum entropy model. In the experiment, the proposed model showed better performances than previous works in speech act classification. In addition, the proposed model showed high performances in concept sequence classification. Based on these experimental results, we believe that the proposed model will be more helpful to a dialogue system than previous speech act classification models because it can return speech acts and concept sequences at the same time on the same framework. [ABSTRACT FROM AUTHOR]

An audio-assisted system is investigated that detects if a movie scene is a dialogue or not. The system is based on actor indicator functions. That is, functions which define if an actor speaks at a certain time instant. In particular, the cross-correlation and the magnitude of the corresponding the cross-power spectral density of a pair of indicator functions are input to various classifiers, such as voted perceptrons, radial basis function networks, random trees, and support vector machines for dialogue/non-dialogue detection. To boost classifier efficiency AdaBoost is also exploited. The aforementioned classifiers are trained using ground truth indicator functions determined by human annotators for 41 dialogue and another 20 non-dialogue audio instances. For testing, actual indicator functions are derived by applying audio activity detection and actor clustering to audio recordings. 23 instances are randomly chosen among the aforementioned 41 dialogue instances, 17 of which correspond to dialogue scenes and 6 to non-dialogue ones. Accuracy ranging between 0.739 and 0.826 is reported. [ABSTRACT FROM AUTHOR]

Spoken dialogue systems must inevitably deal with out-of-grammar utterances. We address this problem in multi-domain spoken dialogue systems, which deal with more tasks than a single-domain system. We defined a topic by augmenting a domain about which users want to find more information, and we developed a method of recovering out-of-grammar utterances based on topic estimation, i.e., by providing a help message in the estimated domain. Moreover, domain extensibility, that is, the ability to add new domains to the system, should be inherently retained in multi-domain systems. To estimate domains without sacrificing extensibility, we collected documents from the Web as training data. Since the data contained a certain amount of noise, we used latent semantic mapping (LSM), which enables robust topic estimation by removing the effects of noise from the data. Experimental results showed that our method improved topic estimation accuracy by 23.2 points for data including out-of-grammar utterances. [Copyright &y& Elsevier]

User simulations are increasingly employed in the development and evaluation of spoken dialog systems. However, there is no accepted method for evaluating user simulations, which is problematic because the performance of new dialog management techniques is often evaluated on user simulations alone, not on real people. In this paper, we propose a novel method of evaluating user simulations. We view a user simulation as a predictor of the performance of a dialog system, where per-dialog performance is measured with a domain-specific scoring function. The divergence between the distribution of dialog scores in the real and simulated corpora provides a measure of the quality of the user simulation, and we argue that the Cramér–von Mises divergence is well-suited to this task. To demonstrate this technique, we study a corpus of callers with real information needs and show that Cramér-von Mises divergence conforms to expectations. Finally, we present simple tools which enable practitioners to interpret the statistical significance of comparisons between user simulations. [Copyright &y& Elsevier]