Report on the Second NLG Challenge on Generating Instructions in Virtual Environments (GIVE-2)

We describe the second installment of the Challenge on Generating Instructions in Virtual Environments (GIVE-2), a shared task for the NLG community which took place in 2009-10. We evaluated seven NLG systems by connecting them to 1825 users over the Internet, and report the results of this evaluation in terms of objective and subjective measures

Generating Instructions in a 3D Game Environment: Efficiency or Entertainment?

The GIVE Challenge was designed for the evaluation of natural language generation (NLG) systems. It involved the automatic generation of instructions for users in a 3D environment. In this paper we introduce two NLG systems that we developed for this challenge. One system focused on generating optimally helpful instructions while the other focused on entertainment. We used the data gathered in the Challenge to compare the efficiency and entertainment value of both systems. We found a clear difference in efficiency, but were unable to prove that one system was more entertaining than the other. This could be explained by the fact that the set-up and evaluation methods of the GIVE Challenge were not aimed at entertainment

Shared task proposal: Instruction giving in virtual worlds

This paper reports on the results of the working group “Virtual Environ-ments ” at the Workshop on Shared Tasks and Comparative Evaluation for NLG. This working group discussed the use of virtual environments as a platform for NLG evaluation, and more specifically of the generation of in

Reference and the facilitation of search in spatial domains

This is a pre-final version of the article, whose official publication is expected in the winter of 2013-14.Peer reviewedPreprin

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

Augmenting Situated Spoken Language Interaction with Listener Gaze

Collaborative task solving in a shared environment requires referential success. Human speakers follow the listener’s behavior in order to monitor language comprehension (Clark, 1996). Furthermore, a natural language generation (NLG) system can exploit listener gaze to realize an effective interaction strategy by responding to it with verbal feedback in virtual environments (Garoufi, Staudte, Koller, & Crocker, 2016). We augment situated spoken language interaction with listener gaze and investigate its role in human-human and human-machine interactions. Firstly, we evaluate its impact on prediction of reference resolution using a mulitimodal corpus collection from virtual environments. Secondly, we explore if and how a human speaker uses listener gaze in an indoor guidance task, while spontaneously referring to real-world objects in a real environment. Thirdly, we consider an object identification task for assembly under system instruction. We developed a multimodal interactive system and two NLG systems that integrate listener gaze in the generation mechanisms. The NLG system “Feedback” reacts to gaze with verbal feedback, either underspecified or contrastive. The NLG system “Installments” uses gaze to incrementally refer to an object in the form of installments. Our results showed that gaze features improved the accuracy of automatic prediction of reference resolution. Further, we found that human speakers are very good at producing referring expressions, and showing listener gaze did not improve performance, but elicited more negative feedback. In contrast, we showed that an NLG system that exploits listener gaze benefits the listener’s understanding. Specifically, combining a short, ambiguous instruction with con- trastive feedback resulted in faster interactions compared to underspecified feedback, and even outperformed following long, unambiguous instructions. Moreover, alternating the underspecified and contrastive responses in an interleaved manner led to better engagement with the system and an effcient information uptake, and resulted in equally good performance. Somewhat surprisingly, when gaze was incorporated more indirectly in the generation procedure and used to trigger installments, the non-interactive approach that outputs an instruction all at once was more effective. However, if the spatial expression was mentioned first, referring in gaze-driven installments was as efficient as following an exhaustive instruction. In sum, we provide a proof of concept that listener gaze can effectively be used in situated human-machine interaction. An assistance system using gaze cues is more attentive and adapts to listener behavior to ensure communicative success

Natural language generation for social robotics: Opportunities and challenges

In the increasingly popular and diverse research area of social robotics, the primary goal is to develop robot agents that exhibit socially intelligent behaviour while interacting in a face-to-face context with human partners. An important aspect of face-to-face social conversation is fluent, flexible linguistic interaction: as Bavelas et al. [1] point out, face-to-face dialogue is both the basic form of human communication and the richest and most flexible, combining unrestricted verbal expression with meaningful non-verbal acts such as gestures and facial displays, along with instantaneous, continuous collaboration between the speaker and the listener. In practice, however, most developers of social robots tend not to use the full possibilities of the unrestricted verbal expression afforded by face-to-face conversation; instead, they generally tend to employ relatively simplistic processes for choosing the words for their robots to say. This contrasts with the work carried out Natural Language Generation (NLG), the field of computational linguistics devoted to the automated production of high-quality linguistic content: while this research area is also an active one, in general most effort in NLG is focussed on producing high-quality written text. This article summarises the state-of-the-art in the two individual research areas of social robotics and natural language generation. It then discusses the reasons why so few current social robots make use of more sophisticated generation techniques. Finally, an approach is proposed to bringing some aspects of NLG into social robotics, concentrating on techniques and tools that are most appropriate to the needs of socially interactive robots