Four Mode Based Dialogue Management with Modified POMDP Model

This thesis proposes a method to manage the interaction between the user and the system dynamically, through speech or text input which updates the user goals, select system actions and calculate rewards for each system response at each time-stamp. The main focus is made on the dialog manager, which decides how to continue the dialogue. We have used POMDP technique, as it maintains a belief distribution on the dialogue states based on the observations over the dialogue even in a noisy environment. Four contextual control modes are introduced in dialogue management for decision-making mechanism, and to keep track of machine behaviour for each dialogue state. The result obtained proves that our proposed framework has overcome the limitations of prior POMDP methods, and exactly understands the actual intention of the users within the available time, providing very interactive conversation between the user and the computer

Introduction for speech and language for interactive robots

This special issue includes research articles which apply spoken language processing to robots that interact with human users through speech, possibly combined with other modalities. Robots that can listen to human speech, understand it, interact according to the conveyed meaning, and respond represent major research and technological challenges. Their common aim is to equip robots with natural interaction abilities. However, robotics and spoken language processing are areas that are typically studied within their respective communities with limited communication across disciplinary boundaries. The articles in this special issue represent examples that address the need for an increased multidisciplinary exchange of ideas

Online learning and transfer for user adaptation in dialogue systems

International audienceWe address the problem of user adaptation in Spoken Dialogue Systems. The goal is to quickly adapt online to a new user given a large amount of dialogues collected with other users. Previous works using Transfer for Reinforcement Learning tackled this problem when the number of source users remains limited. In this paper, we overcome this constraint by clustering the source users: each user cluster, represented by its centroid, is used as a potential source in the state-of-the-art Transfer Reinforcement Learning algorithm. Our benchmark compares several clustering approaches , including one based on a novel metric. All experiments are led on a negotiation dialogue task, and their results show significant improvements over baselines

Optimising user experience with: conversational Interfaces

Dissertação de Mestrado em Engenharia InformáticaUser Experience is one of the main aspects that maintain a customer loyal to cloud based solutions or SaaS (Software as a Service). With the rise of the natural language processing techniques, the industry is looking at automated chatbot solutions to boost and expand their services. This thesis presents a practical case study of the implementation of a chatbot solution to complement a CRM (Customer Relationship Management) software called FOXAIO, and then quantify, following the most appropriate guides and solutions available, the User Experience (UX) optimisation. In order to create a robust and scalable solution based on the constraints created by the company in the case, we reviewed the current deep learning techniques, tools and libraries available to help the development process. The most proven techniques in the field of Natural Language Processing (NLP) will be introduced. To achieve the goals of this solution without "reinventing the wheel", we present possible architectures to use at the top of some open source and available tools on the market, with a special relief in the framework RASA. Also we discussed some of possible techniques to create the intent classifier, where we detail the better performance in the top of the rasa tensorflow embedding pipeline for this particular case. The conversational system, also, required a channel to interact with the final user. To achieve that, we also implemented a basic chat interface created on the top of the socket protocol, which communicate with the conversation system. In any case, it would be possible to extend to the other channel’s available on the market, like messenger, slack, telegram. Finally, we detail with a few use cases, that’s hypothetically possible to improve the user experience of an existing software system (FOXAIO) using a conversational interface on the top of that. Also, we achieved some highlights about the preference to use a conversational interface because of his simplicity, defended by a better score in the SUS scale, 70 against 58 to the traditional UI, and good indicatives by the HEART framework.O User Experience é possivelmente um dos principais aspetos para fidelizar um cliente numa solução cloud, as chamadas soluções SaaS (Software as a Service). O crescimento acentuado deste tipo de soluções aquece a rivalidade entre competidores e cada vez mais pretende-se oferecer as formas mais revolucionárias para premiar a qualidade de um serviço. Com o crescimento acentuado das técnicas na área do NLP (Natural Language Processing) a indústria começa a olhar para os chatbots como uma possível solução de automatizar, impulsionar e expandir as suas ofertas. A presente tese visa a apresentar uma implementação prática de um chatbot sobre um software com semelhanças de um CRM (Customer Relationship Management) existente intitulado por FOXAIO. Com o objetivo de desenvolver uma solução robusta e escalável tendo em atenção as condições elaboradas pela empresa em questão, um longo e detalhado estudo foi elaborado sobre as mais diversas técnicas de deep learning usadas no ramo de Processamento de Linguagem Natural (NLP). Atribuindo um particular ênfase às redes neurais recorrentes (RNN) e com a devida extensão Long Short Term Memory (LSTM) que juntas, formam e trabalham muito bem na resolução dos problemas de um sistema de inteligência artificial, como é o caso. Para a sua implementação sobre um software já existente, foi necessário o desenvolvimento de uma pequena interface conversacional com o objetivo de mais tarde a complementar sobre a interface do utilizador do mesmo. Para esse efeito, foi implementado um canal sobre o sistema conversacional de comunicação em protocolo de socket, criando uma classe para o efeito que mais tarde seria útil para gerar logs de análise. Durante a implementação do sistema conversacional foram feitas várias comparações sobre as variantes dos seus módulos desde o Dialog Management (DM) ao Intent Classifier onde várias arquiteturas foram expostas e comparadas com o intuito de corresponder à melhor solução possível para um chatbot de língua portuguesa em primeira instância, foi optado pela escolha de um Dialog Management híbrido face ao domínio e à existência de conversas contextuais contínuas onde, por exemplo, se torna bastante difícil de desenvolver sobre outros paradigmas. Quanto ao Intent Classifier, foi usada a técnica rasa tensorflow embedding, esta técnica (que treina palavras do princípio) usada obteve melhores resultados para o particular caso estudado na presente tese (CRM), do que por exemplo o uso um modelo de dados com palavras já treinadas. Finalmente, conseguimos apresentar hipoteticamente, possíveis melhorias do UX no uso de uma interface conversacional sobre uma interface tradicional, usando as várias ferramentas de análise disponíveis, onde por exemplo com o auxílio da framework HEART (criada pelo Google), conseguimos obter indicativos bastante satisfatórios por 34 pessoas que fizeram os primeiros testes no chatbot desenvolvido. Examinando o feedback desses mesmos utilizadores em ambiente de teste, conseguimos obter um resultado na escala de SUS (System Usability Scale) com um valor de 70, enquanto a interface tradicional arrecadou 58, notando então que as pessoas se sentiram mais capazes no uso do sistema conversacional

Training Dialogue Systems With Human Advice

International audienceOne major drawback of Reinforcement Learning (RL) Spoken Dialogue Systems is that they inherit from the general explorationrequirements of RL which makes them hard to deploy from an industry perspective. On the other hand, industrial systems rely onhuman expertise and hand written rules so as to avoid irrelevant behavior to happen and maintain acceptable experience from theuser point of view. In this paper, we attempt to bridge the gap between those two worlds by providing an easy way to incorporate allkinds of human expertise in the training phase of a Reinforcement Learning Dialogue System. Our approach, based on the TAMERframework, enables safe and efficient policy learning by combining the traditional Reinforcement Learning reward signal with anadditional reward, encoding expert advice. Experimental results show that our method leads to substantial improvements over moretraditional Reinforcement Learning methods

Dialogue manager domain adaptation using Gaussian process reinforcement learning

Spoken dialogue systems allow humans to interact with machines using natural speech. As such, they have many benefits. By using speech as the primary communication medium, a computer interface can facilitate swift, human-like acquisition of information. In recent years, speech interfaces have become ever more popular, as is evident from the rise of personal assistants such as Siri, Google Now, Cortana and Amazon Alexa. Recently, data-driven machine learning methods have been applied to dialogue modelling and the results achieved for limited-domain applications are comparable to or outperform traditional approaches. Methods based on Gaussian processes are particularly effective as they enable good models to be estimated from limited training data. Furthermore, they provide an explicit estimate of the uncertainty which is particularly useful for reinforcement learning. This article explores the additional steps that are necessary to extend these methods to model multiple dialogue domains. We show that Gaussian process reinforcement learning is an elegant framework that naturally supports a range of methods, including prior knowledge, Bayesian committee machines and multi-agent learning, for facilitating extensible and adaptable dialogue systems.Engineering and Physical Sciences Research Council (Grant ID: EP/M018946/1 ”Open Domain Statistical Spoken Dialogue Systems”

From art for arts sake to art as means of knowing:a rationale for advancing arts-based methods in research, practice and pedagogy

This paper advances a philosophically informed rationale for the broader, reflexive and practical application of arts-based methods to benefit research, practice and pedagogy. It addresses the complexity and diversity of learning and knowing, foregrounding a cohabitative position and recognition of a plurality of research approaches, tailored and responsive to context. Appreciation of art and aesthetic experience is situated in the everyday, underpinned by multi-layered exemplars of pragmatic visual-arts narrative inquiry undertaken in the third, creative and communications sectors. Discussion considers semi-guided use of arts-based methods as a conduit for topic engagement, reflection and intersubjective agreement; alongside observation and interpretation of organically employed approaches used by participants within daily norms. Techniques span handcrafted (drawing), digital (photography), hybrid (cartooning), performance dimensions (improvised installations) and music (metaphor and structure). The process of creation, the artefact/outcome produced and experiences of consummation are all significant, with specific reflexivity impacts. Exploring methodology and epistemology, both the "doing" and its interpretation are explicated to inform method selection, replication, utility, evaluation and development of cross-media skills literacy. Approaches are found engaging, accessible and empowering, with nuanced capabilities to alter relationships with phenomena, experiences and people. By building a discursive space that reduces barriers; emancipation, interaction, polyphony, letting-go and the progressive unfolding of thoughts are supported, benefiting ways of knowing, narrative (re)construction, sensory perception and capacities to act. This can also present underexplored researcher risks in respect to emotion work, self-disclosure, identity and agenda. The paper therefore elucidates complex, intricate relationships between form and content, the represented and the representation or performance, researcher and participant, and the self and other. This benefits understanding of phenomena including personal experience, sensitive issues, empowerment, identity, transition and liminality. Observations are relevant to qualitative and mixed methods researchers and a multidisciplinary audience, with explicit identification of challenges, opportunities and implications

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