Robust Dialog Management Through A Context-centric Architecture

This dissertation presents and evaluates a method of managing spoken dialog interactions with a robust attention to fulfilling the human user’s goals in the presence of speech recognition limitations. Assistive speech-based embodied conversation agents are computer-based entities that interact with humans to help accomplish a certain task or communicate information via spoken input and output. A challenging aspect of this task involves open dialog, where the user is free to converse in an unstructured manner. With this style of input, the machine’s ability to communicate may be hindered by poor reception of utterances, caused by a user’s inadequate command of a language and/or faults in the speech recognition facilities. Since a speech-based input is emphasized, this endeavor involves the fundamental issues associated with natural language processing, automatic speech recognition and dialog system design. Driven by ContextBased Reasoning, the presented dialog manager features a discourse model that implements mixed-initiative conversation with a focus on the user’s assistive needs. The discourse behavior must maintain a sense of generality, where the assistive nature of the system remains constant regardless of its knowledge corpus. The dialog manager was encapsulated into a speech-based embodied conversation agent platform for prototyping and testing purposes. A battery of user trials was performed on this agent to evaluate its performance as a robust, domain-independent, speech-based interaction entity capable of satisfying the needs of its users

Motivations, Classification and Model Trial of Conversational Agents for Insurance Companies

Advances in artificial intelligence have renewed interest in conversational agents. So-called chatbots have reached maturity for industrial applications. German insurance companies are interested in improving their customer service and digitizing their business processes. In this work we investigate the potential use of conversational agents in insurance companies by determining which classes of agents are of interest to insurance companies, finding relevant use cases and requirements, and developing a prototype for an exemplary insurance scenario. Based on this approach, we derive key findings for conversational agent implementation in insurance companies.Comment: 12 pages, 6 figure, accepted for presentation at The International Conference on Agents and Artificial Intelligence 2019 (ICAART 2019

Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time

Crowd-powered conversational assistants have been shown to be more robust than automated systems, but do so at the cost of higher response latency and monetary costs. A promising direction is to combine the two approaches for high quality, low latency, and low cost solutions. In this paper, we introduce Evorus, a crowd-powered conversational assistant built to automate itself over time by (i) allowing new chatbots to be easily integrated to automate more scenarios, (ii) reusing prior crowd answers, and (iii) learning to automatically approve response candidates. Our 5-month-long deployment with 80 participants and 281 conversations shows that Evorus can automate itself without compromising conversation quality. Crowd-AI architectures have long been proposed as a way to reduce cost and latency for crowd-powered systems; Evorus demonstrates how automation can be introduced successfully in a deployed system. Its architecture allows future researchers to make further innovation on the underlying automated components in the context of a deployed open domain dialog system.Comment: 10 pages. To appear in the Proceedings of the Conference on Human Factors in Computing Systems 2018 (CHI'18

Social talk capabilities for dialogue systems

Small talk capabilities are an important but very challenging extension to dialogue systems. Small talk (or “social talk”) refers to a kind of conversation, which does not focus on the exchange of information, but on the negotiation of social roles and situations. The goal of this thesis is to provide knowledge, processes and structures that can be used by dialogue systems to satisfactorily participate in social conversations. For this purpose the thesis presents research in the areas of natural-language understanding, dialogue management and error handling. Nine new models of social talk based on a data analysis of small talk conversations are described. The functionally-motivated and content-abstract models can be used for small talk conversations on various topics. The basic elements of the models consist of dialogue acts for social talk newly developed on basis of social science theory. The thesis also presents some conversation strategies for the treatment of so-called “out-of-domain” (OoD) utterances that can be used to avoid errors in the input understanding of dialogue systems. Additionally, the thesis describes a new extension to dialogue management that flexibly manages interwoven dialogue threads. The small talk models as well as the strategies for handling OoD utterances are encoded as computational dialogue threads

Social talk capabilities for dialogue systems

Small talk capabilities are an important but very challenging extension to dialogue systems. Small talk (or social talk) refers to a kind of conversation, which does not focus on the exchange of information, but on the negotiation of social roles and situations. The goal of this thesis is to provide knowledge, processes and structures that can be used by dialogue systems to satisfactorily participate in social conversations. For this purpose the thesis presents research in the areas of natural-language understanding, dialogue management and error handling. Nine new models of social talk based on a data analysis of small talk conversations are described. The functionally-motivated and content-abstract models can be used for small talk conversations on various topics. The basic elements of the models consist of dialogue acts for social talk newly developed on basis of social science theory. The thesis also presents some conversation strategies for the treatment of so-called out-of-domain (OoD) utterances that can be used to avoid errors in the input understanding of dialogue systems. Additionally, the thesis describes a new extension to dialogue management that flexibly manages interwoven dialogue threads. The small talk models as well as the strategies for handling OoD utterances are encoded as computational dialogue threads

Software-based dialogue systems: Survey, taxonomy and challenges

The use of natural language interfaces in the field of human-computer interaction is undergoing intense study through dedicated scientific and industrial research. The latest contributions in the field, including deep learning approaches like recurrent neural networks, the potential of context-aware strategies and user-centred design approaches, have brought back the attention of the community to software-based dialogue systems, generally known as conversational agents or chatbots. Nonetheless, and given the novelty of the field, a generic, context-independent overview on the current state of research of conversational agents covering all research perspectives involved is missing. Motivated by this context, this paper reports a survey of the current state of research of conversational agents through a systematic literature review of secondary studies. The conducted research is designed to develop an exhaustive perspective through a clear presentation of the aggregated knowledge published by recent literature within a variety of domains, research focuses and contexts. As a result, this research proposes a holistic taxonomy of the different dimensions involved in the conversational agents’ field, which is expected to help researchers and to lay the groundwork for future research in the field of natural language interfaces.With the support from the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund. The corresponding author gratefully acknowledges the Universitat Politècnica de Catalunya and Banco Santander for the inancial support of his predoctoral grant FPI-UPC. This paper has been funded by the Spanish Ministerio de Ciencia e Innovación under project / funding scheme PID2020-117191RB-I00 / AEI/10.13039/501100011033.Peer ReviewedPostprint (author's final draft