Measuring the differences between human-human and human-machine dialogs

In this paper, we assess the applicability of user simulation techniques to generate dialogs which are similar to real human-machine spoken interactions.To do so, we present the results of the comparison between three corpora acquired by means of different techniques. The first corpus was acquired with real users.A statistical user simulation technique has been applied to the same task to acquire the second corpus. In this technique, the next user answer is selected by means of a classification process that takes into account the previous dialog history, the lexical information in the clause, and the subtask of the dialog to which it contributes. Finally, a dialog simulation technique has been developed for the acquisition of the third corpus. This technique uses a random selection of the user and system turns, defining stop conditions for automatically deciding if the simulated dialog is successful or not. We use several evaluation measures proposed in previous research to compare between our three acquired corpora, and then discuss the similarities and differences with regard to these measures

An automatic dialog simulation technique to develop and evaluate interactive conversational agents

During recent years, conversational agents have become a solution to provide straightforward and more natural ways of retrieving information in the digital domain. In this article, we present an agent-based dialog simulation technique for learning new dialog strategies and evaluating conversational agents. Using this technique, the effort necessary to acquire data required to train the dialog model and then explore new dialog strategies is considerably reduced. A set of measures has also been defined to evaluate the dialog strategy that is automatically learned and to compare different dialog corpora. We have applied this technique to explore the space of possible dialog strategies and evaluate the dialogs acquired for a conversational agent that collects monitored data from patients suffering from diabetes. The results of the comparison of these measures for an initial corpus and a corpus acquired using the dialog simulation technique show that the conversational agent reduces the time needed to complete the dialogs and improve their quality, thereby allowing the conversational agent to tackle new situations and generate new coherent answers for the situations already present in an initial model.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS S2009/TIC-1485Publicad

Bringing together commercial and academic perspectives for the development of intelligent AmI interfaces

The users of Ambient Intelligence systems expect an intelligent behavior from their environment, receiving adapted and easily accessible services and functionality. This can only be possible if the communication between the user and the system is carried out through an interface that is simple (i.e. which does not have a steep learning curve), fluid (i.e. the communication takes place rapidly and effectively), and robust (i.e. the system understands the user correctly). Natural language interfaces such as dialog systems combine the previous three requisites, as they are based on a spoken conversation between the user and the system that resembles human communication. The current industrial development of commercial dialog systems deploys robust interfaces in strictly defined application domains. However, commercial systems have not yet adopted the new perspective proposed in the academic settings, which would allow straightforward adaptation of these interfaces to various application domains. This would be highly beneficial for their use in AmI settings as the same interface could be used in varying environments. In this paper, we propose a new approach to bridge the gap between the academic and industrial perspectives in order to develop dialog systems using an academic paradigm while employing the industrial standards, which makes it possible to obtain new generation interfaces without the need for changing the already existing commercial infrastructures. Our proposal has been evaluated with the successful development of a real dialog system that follows our proposed approach to manage dialog and generates code compliant with the industry-wide standard VoiceXML.Research funded by projects CICYT TIN2011-28620-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485), and DPS2008- 07029-C02-02.Publicad

Unsupervised Controllable Text Formalization

We propose a novel framework for controllable natural language transformation. Realizing that the requirement of parallel corpus is practically unsustainable for controllable generation tasks, an unsupervised training scheme is introduced. The crux of the framework is a deep neural encoder-decoder that is reinforced with text-transformation knowledge through auxiliary modules (called scorers). The scorers, based on off-the-shelf language processing tools, decide the learning scheme of the encoder-decoder based on its actions. We apply this framework for the text-transformation task of formalizing an input text by improving its readability grade; the degree of required formalization can be controlled by the user at run-time. Experiments on public datasets demonstrate the efficacy of our model towards: (a) transforming a given text to a more formal style, and (b) introducing appropriate amount of formalness in the output text pertaining to the input control. Our code and datasets are released for academic use.Comment: AAA

Bringing context-aware access to the web through spoken interaction

The web has become the largest repository of multimedia information and its convergence with telecommunications is now bringing the benefits of web technology to hand-held devices. To optimize data access using these devices and provide services which meet the user needs through intelligent information retrieval, the system must sense and interpret the user environment and the communication context. In addition, natural spoken conversation with handheld devices makes possible the use of these applications in environments in which the use of GUI interfaces is not effective, provides a more natural human-computer interaction, and facilitates access to the web for people with visual or motor disabilities, allowing their integration and the elimination of barriers to Internet access. In this paper, we present an architecture for the design of context-aware systems that use speech to access web services. Our contribution focuses specifically on the use of context information to improve the effectiveness of providing web services by using a spoken dialog system for the user-system interaction. We also describe an application of our proposal to develop a context-aware railway information system, and provide a detailed evaluation of the influence of the context information in the quality of the services that are supplied.Research funded by projects CICYT TIN2011-28620-C02-01, CICYT TEC 2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485), and DPS2008-07029-C02-02.Publicad

A framework for improving error detection and correction in spoken dialog systems

Despite The Recent Improvements In Performance And Reliably Of The Different Components Of Dialog Systems, It Is Still Crucial To Devise Strategies To Avoid Error Propagation From One Another. In This Paper, We Contribute A Framework For Improved Error Detection And Correction In Spoken Conversational Interfaces. The Framework Combines User Behavior And Error Modeling To Estimate The Probability Of The Presence Of Errors In The User Utterance. This Estimation Is Forwarded To The Dialog Manager And Used To Compute Whether It Is Necessary To Correct Possible Errors. We Have Designed An Strategy Differentiating Between The Main Misunderstanding And Non-Understanding Scenarios, So That The Dialog Manager Can Provide An Acceptable Tailored Response When Entering The Error Correction State. As A Proof Of Concept, We Have Applied Our Proposal To A Customer Support Dialog System. Our Results Show The Appropriateness Of Our Technique To Correctly Detect And React To Errors, Enhancing The System Performance And User Satisfaction.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485)

A network model of interpersonal alignment in dialog

In dyadic communication, both interlocutors adapt to each other linguistically, that is, they align interpersonally. In this article, we develop a framework for modeling interpersonal alignment in terms of the structural similarity of the interlocutors’ dialog lexica. This is done by means of so-called two-layer time-aligned network series, that is, a time-adjusted graph model. The graph model is partitioned into two layers, so that the interlocutors’ lexica are captured as subgraphs of an encompassing dialog graph. Each constituent network of the series is updated utterance-wise. Thus, both the inherent bipartition of dyadic conversations and their gradual development are modeled. The notion of alignment is then operationalized within a quantitative model of structure formation based on the mutual information of the subgraphs that represent the interlocutor’s dialog lexica. By adapting and further developing several models of complex network theory, we show that dialog lexica evolve as a novel class of graphs that have not been considered before in the area of complex (linguistic) networks. Additionally, we show that our framework allows for classifying dialogs according to their alignment status. To the best of our knowledge, this is the first approach to measuring alignment in communication that explores the similarities of graph-like cognitive representations. Keywords: alignment in communication; structural coupling; linguistic networks; graph distance measures; mutual information of graphs; quantitative network analysi

A Neural Network Approach to Intention Modeling forUser-Adapted Conversational Agents

Spoken dialogue systems have been proposed to enable a more natural and intuitive interaction with the environment andhuman-computer interfaces. In this contribution, we present a framework based on neural networks that allows modeling of theuser’s intention during the dialogue and uses this prediction todynamically adapt the dialoguemodel of the system taking intoconsideration the user’s needs and preferences. We have evaluated our proposal to develop a user-adapted spoken dialogue systemthat facilitates tourist information and services and provide a detailed discussion of the positive influence of our proposal in thesuccess of the interaction, the information and services provided, and the quality perceived by the users