Design of a Controlled Language for Critical Infrastructures Protection

We describe a project for the construction of controlled language for critical infrastructures protection (CIP). This project originates from the need to coordinate and categorize the communications on CIP at the European level. These communications can be physically represented by official documents, reports on incidents, informal communications and plain e-mail. We explore the application of traditional library science tools for the construction of controlled languages in order to achieve our goal. Our starting point is an analogous work done during the sixties in the field of nuclear science known as the Euratom Thesaurus.JRC.G.6-Security technology assessmen

Customisable chatbot as a research instrument

Abstract. Chatbots are proliferating rapidly online for a variety of different purposes. This thesis presents a customisable chatbot that was designed and developed as a research instrument for online customer interaction research. The developed chatbot facilitates creation of different bot personas, data management tools, and a fully functional online chat user interface. Customer-facing bots in the system are rulebased, with basic input processing and text response selection based on best match. The system uses its own database to store user-chatbot dialogue history. Further, bots can be assigned unique dialogue scripts and their profiles can be customised concerning name, description and profile image. In the presented validation studies, participants completed a task by taking part in a conversation with different bots, as hosted by the system and invoked through distinct URL parameters. Second, the participants filled in a questionnaire on their experience with the bot, designed to reveal differences in how the bots were perceived. Our results suggest that the chatbot’s personality impacted how customers experienced the interactions. Therefore, the developed system can facilitate research scenarios that deal with investigating participant responses to different chatbot personas. Future work is necessary for a wider range of applications and enhanced response control.Personoitava chatbot tutkimustyökaluna. Tiivistelmä. Chatbotit yleistyvät nopeasti Internetissä ja niitä käytetään enenevissä määrin useissa eri käyttötarkoituksissa. Tämä diplomityö esittelee personoitavan chatbotin, joka on kehitetty tutkimustyökaluksi verkon yli tapahtuvaan vuorovaikutustutkimukseen. Kehitetty chatbot sisältää erilaisten bottipersoonien luonnin, apuvälineitä datan käsittelyn, ja itse botin käyttöliittymän. Järjestelmän käyttäjille vastailevat bottipersoonat ovat sääntöihin perustuvia, niiden syötteet käsitellään suoraviivaisesti ja vastaukseksi valitaan vertailun mukaan paras ennaltamääritellyn skriptin mukaisesti. Järjestelmä käyttää omaa tietokantaa tallentamaan käyttäjä-botti keskusteluhistorian. Lisäksi boteille voidaan asettaa uniikki dialogimalli, ja niiden profiilista voidaan personoida URL-parametrillä nimi, botin kuvaus ja profiilikuva. Chatbotin tekninen toiminta todettiin tutkimuksella, jossa osallistujat suorittivat annetun tehtävän seuraamalla osittain valmista käsikirjoitusta eri bottien kanssa. Tämän jälkeen osallistujat täyttivät käyttäjäkyselyn liittyen heidän kokemukseensa botin kanssa. Kysely oli suunniteltu paljastamaan mahdolliset eroavaisuudet siinä, kuinka botin käyttäytyminen miellettiin keskustelun aikana. Käyttäjätestin tulokset viittaavat siihen, että chatbotin persoonalla oli vaikutus käyttäjien kokemukseen. Kehitetty järjestelmä siis pystyy mahdollistamaan tutkimusasetelmia, joissa tutkitaan osallistujien reaktioita erilaisten chattibottien persooniin. Jatkotyö kehitetyn chatbotin yhteydessä keskittyy monimutkaisempien käyttötarkoitusten lisäämiseen ja botin vastausten parantamiseen edistyksellisemmän luonnollisen kielen käsittelyn avulla

Data-Driven Policy Optimisation for Multi-Domain Task-Oriented Dialogue

Recent developments in machine learning along with a general shift in the public attitude towards digital personal assistants has opened new frontiers for conversational systems. Nevertheless, building data-driven multi-domain conversational agents that act optimally given a dialogue context is an open challenge. The first step towards that goal is developing an efficient way of learning a dialogue policy in new domains. Secondly, it is important to have the ability to collect and utilise human-human conversational data to bootstrap an agent's knowledge. The work presented in this thesis demonstrates how a neural dialogue manager fine-tuned with reinforcement learning presents a viable approach for learning a dialogue policy efficiently and across many domains. The thesis starts by introducing a dialogue management module that learns through interactions to act optimally given a current context of a conversation. The current shift towards neural, parameter-rich systems does not fully address the problem of error noise coming from speech recognition or natural language understanding components. A Bayesian approach is therefore proposed to learn more robust and effective policy management in direct interactions without any prior data. By putting a distribution over model weights, the learning agent is less prone to overfit to particular dialogue realizations and a more efficient exploration policy can be therefore employed. The results show that deep reinforcement learning performs on par with non-parametric models even in a low data regime while significantly reducing the computational complexity compared with the previous state-of-the-art. The deployment of a dialogue manager without any pre-training on human conversations is not a viable option from an industry perspective. However, the progress in building statistical systems, particularly dialogue managers, is hindered by the scale of data available. To address this fundamental obstacle, a novel data-collection pipeline entirely based on crowdsourcing without the need for hiring professional annotators is introduced. The validation of the approach results in the collection of the Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully labeled collection of human-human written conversations spanning over multiple domains and topics. The proposed dataset creates a set of new benchmarks (belief tracking, policy optimisation, and response generation) significantly raising the complexity of analysed dialogues. The collected dataset serves as a foundation for a novel reinforcement learning (RL)-based approach for training a multi-domain dialogue manager. A Multi-Action and Slot Dialogue Agent (MASDA) is proposed to combat some limitations: 1) handling complex multi-domain dialogues with multiple concurrent actions present in a single turn; and 2) lack of interpretability, which consequently impedes the use of intermediate signals (e.g., dialogue turn annotations) if such signals are available. MASDA explicitly models system acts and slots using intermediate signals, resulting in an improved task-based end-to-end framework. The model can also select concurrent actions in a single turn, thus enriching the representation of the generated responses. The proposed framework allows for RL training of dialogue task completion metrics when dealing with concurrent actions. The results demonstrate the advantages of both 1) handling concurrent actions and 2) exploiting intermediate signals: MASDA outperforms previous end-to-end frameworks while also offering improved scalability.EPSR

Recurrent Neural Network Language Generation for Dialogue Systems

Language is the principal medium for ideas, while dialogue is the most natural and effective way for humans to interact with and access information from machines. Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact on usability and perceived quality. Many commonly used NLG systems employ rules and heuristics, which tend to generate inflexible and stylised responses without the natural variation of human language. However, the frequent repetition of identical output forms can quickly make dialogue become tedious for most real-world users. Additionally, these rules and heuristics are not scalable and hence not trivially extensible to other domains or languages. A statistical approach to language generation can learn language decisions directly from data without relying on hand-coded rules or heuristics, which brings scalability and flexibility to NLG. Statistical models also provide an opportunity to learn in-domain human colloquialisms and cross-domain model adaptations. A robust and quasi-supervised NLG model is proposed in this thesis. The model leverages a Recurrent Neural Network (RNN)-based surface realiser and a gating mechanism applied to input semantics. The model is motivated by the Long-Short Term Memory (LSTM) network. The RNN-based surface realiser and gating mechanism use a neural network to learn end-to-end language generation decisions from input dialogue act and sentence pairs; it also integrates sentence planning and surface realisation into a single optimisation problem. The single optimisation not only bypasses the costly intermediate linguistic annotations but also generates more natural and human-like responses. Furthermore, a domain adaptation study shows that the proposed model can be readily adapted and extended to new dialogue domains via a proposed recipe. Continuing the success of end-to-end learning, the second part of the thesis speculates on building an end-to-end dialogue system by framing it as a conditional generation problem. The proposed model encapsulates a belief tracker with a minimal state representation and a generator that takes the dialogue context to produce responses. These features suggest comprehension and fast learning. The proposed model is capable of understanding requests and accomplishing tasks after training on only a few hundred human-human dialogues. A complementary Wizard-of-Oz data collection method is also introduced to facilitate the collection of human-human conversations from online workers. The results demonstrate that the proposed model can talk to human judges naturally, without any difficulty, for a sample application domain. In addition, the results also suggest that the introduction of a stochastic latent variable can help the system model intrinsic variation in communicative intention much better.Tsung-Hsien Wen's Ph.D. is supported by Toshiba Research Europe Ltd, Cambridge Research Laborator

Generic semantics-based task-oriented dialogue system framework for human-machine interaction in industrial scenarios

285 p.En Industria 5.0, los trabajadores y su bienestar son cruciales en el proceso de producción. En estecontexto, los sistemas de diálogo orientados a tareas permiten que los operarios deleguen las tareas mássencillas a los sistemas industriales mientras trabajan en otras más complejas. Además, la posibilidad deinteractuar de forma natural con estos sistemas reduce la carga cognitiva para usarlos y genera aceptaciónpor parte de los usuarios. Sin embargo, la mayoría de las soluciones existentes no permiten unacomunicación natural, y las técnicas actuales para obtener dichos sistemas necesitan grandes cantidadesde datos para ser entrenados, que son escasos en este tipo de escenarios. Esto provoca que los sistemas dediálogo orientados a tareas en el ámbito industrial sean muy específicos, lo que limita su capacidad de sermodificados o reutilizados en otros escenarios, tareas que están ligadas a un gran esfuerzo en términos detiempo y costes. Dados estos retos, en esta tesis se combinan Tecnologías de la Web Semántica contécnicas de Procesamiento del Lenguaje Natural para desarrollar KIDE4I, un sistema de diálogo orientadoa tareas semántico para entornos industriales que permite una comunicación natural entre humanos ysistemas industriales. Los módulos de KIDE4I están diseñados para ser genéricos para una sencillaadaptación a nuevos casos de uso. La ontología modular TODO es el núcleo de KIDE4I, y se encarga demodelar el dominio y el proceso de diálogo, además de almacenar las trazas generadas. KIDE4I se haimplementado y adaptado para su uso en cuatro casos de uso industriales, demostrando que el proceso deadaptación para ello no es complejo y se beneficia del uso de recursos