Human-robot interactions situated in a dynamic environment create a unique mix of challenges for conversational systems. We argue that, on the one hand, NLG can contribute to addressing these challenges and that, on the other hand, they pose interesting research problems for NLG. To illustrate our position we describe our research on non-humanoid robots using non-verbal signals to support communication.

Recent studies in metaphor extraction across several languages (Broadwell et al., 2013; Strzalkowski et al., 2013) have shown that word imageability ratings are highly correlated with the presence of metaphors in text. Information about imageability of words can be obtained from the MRC Psycholinguistic Database (MRCPD) for English words and Léxico Informatizado del Español Programa (LEXESP) for Spanish words, which is a collection of human ratings obtained in a series of controlled surveys. Unfortunately, word imageability ratings were collected for only a limited number of words: 9,240 words in English, 6,233 in Spanish; and are unavailable at all in the other two languages studied: Russian and Farsi. The present study describes an automated method for expanding the MRCPD by conferring imageability ratings over the synonyms and hyponyms of existing MRCPD words, as identified in Wordnet. The result is an expanded MRCPD+ database with imagea-bility scores for more than 100,000 words. The appropriateness of this expansion process is assessed by examining the structural coherence of the expanded set and by validating the expanded lexicon against human judgment. Finally, the performance of the metaphor extraction system is shown to improve significantly with the expanded database. This paper describes the process for English MRCPD+ and the resulting lexical resource. The process is analogous for other languages.

In this paper, a computational model based on concept polarity is proposed to investigate the influence of communications across the diacultural groups. The hypothesis of this work is that there are communities or groups which can be characterized by a network of concepts and the corresponding valuations of those concepts that are agreed upon by the members of the community. We apply an existing research tool, ECO, to generate text representative of each community and create community specific Valuation Concept Networks (VCN). We then compare VCNs across the communities, to attempt to find contentious concepts, which could subsequently be the focus of further exploration as points of contention between the two communities. A prototype, CPAM (Changing Positions, Altering Minds), was implemented as a proof of concept for this approach. The experiment was conducted using blog data from pro-Palestinian and pro-Israeli communities. A potential application of this method and future work are discussed as well.

In this paper, we describe our experience with collecting and creating an annotated corpus of multi-party online conversations in a chat-room environment. This effort is part of a larger project to develop computational models of social phenomena such as agenda control, influence, and leadership in on-line interactions. Such models will help capturing the dialogue dynamics that are essential for developing, among others, realistic human-machine dialogue systems, including autonomous virtual chat agents. In this paper we describe data collection method used and the characteristics of the initial dataset of English chat. We have devised a multi-tiered collection process in which the subjects start from simple, free-flowing conversations and progress towards more complex and structured interactions. In this paper, we report on the first two stages of this process, which were recently completed. The third, large-scale collection effort is currently being conducted. All English dialogue has been annotated at four levels: communication links, dialogue acts, local topics and meso-topics. Some details of these annotations will be discussed later in this paper, although a full description is impossible within the scope of this article.

Evaluation of complex, collaborative dialogue systems is a difficult task. Traditionally, developers have relied upon subjective feedback from the user, and parametrisation over observable metrics. However, both models place some reliance on the notion of a task; that is, the system is helping to user achieve some clearly defined goal, such as book a flight or complete a banking transaction. It is not clear that such metrics are as useful when dealing with a system that has a more complex task, or even no definable task at all, beyond maintain and performing a collaborative dialogue. Working within the EU funded COMPANIONS program, we investigate the use of appropriateness as a measure of conversation quality, the hypothesis being that good companions need to be good conversational partners . We report initial work in the direction of annotating dialogue for indicators of good conversation, including the annotation and comparison of the output of two generations of the same dialogue system.

Within the EU-funded COMPANIONS project, we are working to evaluate new collaborative conversational models of dialogue. Such an evaluation requires us to benchmark approaches to companionable dialogue. In order to determine the impact of system strategies on our evaluation paradigm, we need to generate a range of companionable conversations, using dialogue strategies such as `empathy' and `positivity'. By companionable dialogue, we mean interactions that take user input of some scenario, and respond in a manner appropriate to the emotional content of the user utterance. In this paper, we describe our working Wizard of Oz (WoZ) system for systematically creating dialogues that fulfil these potential strategies, and enables us to deploy a range of potential techniques for selecting which parts of user input to address is which order, to inform the wizard response to the user based on a manual, on-the-fly assessment of the polarity of the user input.

We present recent work in the area of Cross-Domain Dialogue Act (DA) tagging. We have previously reported on the use of a simple dialogue act classifier based on purely intra-utterance features - principally involving word n-gram cue phrases automatically generated from a training corpus. Such a classifier performs surprisingly well, rivalling scores obtained using far more sophisticated language modelling techniques. In this paper, we apply these automatically extracted cues to a new annotated corpus, to determine the portability and generality of the cues we learn.