Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Personalizing Human-Robot Dialogue Interactions using Face and Name Recognition

Task-oriented dialogue systems are computer systems that aim to provide an interaction indistinguishable from ordinary human conversation with the goal of completing user- defined tasks. They are achieving this by analyzing the intents of users and choosing respective responses. Recent studies show that by personalizing the conversations with this systems one can positevely affect their perception and long-term acceptance. Personalised social robots have been widely applied in different fields to provide assistance. In this thesis we are working on development of a scientific conference assistant. The goal of this assistant is to provide the conference participants with conference information and inform about the activities for their spare time during conference. Moreover, to increase the engagement with the robot our team has worked on personalizing the human-robot interaction by means of face and name recognition. To achieve this personalisation, first the name recognition ability of available physical robot was improved, next by the concent of the participants their pictures were taken and used for memorization of returning users. As acquiring the consent for personal data storage is not an optimal solution, an alternative method for participants recognition using QR Codes on their badges was developed and compared to pre-trained model in terms of speed. Lastly, the personal details of each participant, as unviversity, country of origin, was acquired prior to conference or during the conversation and used in dialogues. The developed robot, called DAGFINN was displayed at two conferences happened this year in Stavanger, where the first time installment did not involve personalization feature. Hence, we conclude this thesis by discussing the influence of personalisation on dialogues with the robot and participants satisfaction with developed social robot

Entertaining and Opinionated but Too Controlling: A Large-Scale User Study of an Open Domain Alexa Prize System

Conversational systems typically focus on functional tasks such as scheduling appointments or creating todo lists. Instead we design and evaluate SlugBot (SB), one of 8 semifinalists in the 2018 AlexaPrize, whose goal is to support casual open-domain social inter-action. This novel application requires both broad topic coverage and engaging interactive skills. We developed a new technical approach to meet this demanding situation by crowd-sourcing novel content and introducing playful conversational strategies based on storytelling and games. We collected over 10,000 conversations during August 2018 as part of the Alexa Prize competition. We also conducted an in-lab follow-up qualitative evaluation. Over-all users found SB moderately engaging; conversations averaged 3.6 minutes and involved 26 user turns. However, users reacted very differently to different conversation subtypes. Storytelling and games were evaluated positively; these were seen as entertaining with predictable interactive structure. They also led users to impute personality and intelligence to SB. In contrast, search and general Chit-Chat induced coverage problems; here users found it hard to infer what topics SB could understand, with these conversations seen as being too system-driven. Theoretical and design implications suggest a move away from conversational systems that simply provide factual information. Future systems should be designed to have their own opinions with personal stories to share, and SB provides an example of how we might achieve this.Comment: To appear in 1st International Conference on Conversational User Interfaces (CUI 2019