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

MultiMediate '22: Backchannel Detection and Agreement Estimation in Group Interactions

Backchannels, i.e. short interjections of the listener, serve important meta-conversational purposes like signifying attention or indicating agreement. Despite their key role, automatic analysis of backchannels in group interactions has been largely neglected so far. The MultiMediate challenge addresses, for the first time, the tasks of backchannel detection and agreement estimation from backchannels in group conversations. This paper describes the MultiMediate challenge and presents a novel set of annotations consisting of 7234 backchannel instances for the MPIIGroupInteraction dataset. Each backchannel was additionally annotated with the extent by which it expresses agreement towards the current speaker. In addition to a an analysis of the collected annotations, we present baseline results for both challenge tasks.Comment: ACM Multimedia 202

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

Building a dialogue system for question-answer forum websites

[EU] Dialogo-sistemak gizakiak laguntzeko sistema automatikoak dira, eta beren ezaugarri nagusia da komunikazioa hizkuntza naturalaren bidez gauzatzeko gai direla. Azken boladan bultzada handia jaso eta eguneroko tresnetan aurkitu daitezke (Siri, Cortana, Alexa, etab.). Sistema hauen erabilera handitu ahala, Community Question Answering (CQA) edo Frequently Asked Questions (FAQ) direlakoak dialogo bitartez atzitzeko interesa zeharo handitu da, bereziki enpresa munduan. Egungo dialogo sistemen elkarrizketarako ahalmena, ordea, oso mugatua da, eskuzko erregelen bidez definituta baitaude. Horrek domeinu berri batean ezartzeko edo behin produkzioan martxan dagoenean monitorizatu eta egokitzeko kostuak handitzen ditu. Bestalde, nahiz eta ikaskuntza sakona bezalako teknikek oso emaitza onak lortu dituzten Hizkuntzaren Prozesamenduko alor desberdinetan, asko sufritzen dute datu eskasiaren arazoa, datu kopuru izugarriak behar baitituzte ikasketarako. Hemen aurkeztutako proiektuaren helburu nagusia aipatutako mugak arintzea da, sare neuronaletan oinarritutako sistema bat inplementatuz eta sistema hauen etorkizuneko garapena bultzatu eta errazteko CQA datu multzo bat sortuz.[EN] Dialogue-systems are automatic systems developed for helping humans in their daily routines. The main characteristic of these systems is that they are able to communicate using natural language. Lately, dialogue agents are becoming increasingly trendy and are already part of our lives as they are implemented in many tools (Siri, Cortana, Alexa...). This incursion of voice agents has increased the interest of accessing Community Question Answering (CQA) and Frequently Asked Questions (FAQ) information by dialogue means, specially in the industrial world. Nowadays, dialogue systems have their conversational ability very limited as they are de ned by hand-crafted rules. This hand-crafted nature, makes domain adaptation an extremely costly and time consuming task. On the other hand, deep learning based techniques, that have achieved state-of-the-art results in many Natural Language Processing (NLP) tasks, sufer from lack of data as they need huge amounts of labelled records for training. So, the main aim of this project, is to develop a neural system together with a CQA dataset for enabling future research in CQA dialogue systems

From Knowledge Augmentation to Multi-tasking: Towards Human-like Dialogue Systems

The goal of building dialogue agents that can converse with humans naturally has been a long-standing dream of researchers since the early days of artificial intelligence. The well-known Turing Test proposed to judge the ultimate validity of an artificial intelligence agent on the indistinguishability of its dialogues from humans'. It should come as no surprise that human-level dialogue systems are very challenging to build. But, while early effort on rule-based systems found limited success, the emergence of deep learning enabled great advance on this topic. In this thesis, we focus on methods that address the numerous issues that have been imposing the gap between artificial conversational agents and human-level interlocutors. These methods were proposed and experimented with in ways that were inspired by general state-of-the-art AI methodologies. But they also targeted the characteristics that dialogue systems possess.Comment: PhD thesi