Turn-taking is essential to the structure of human teamwork. Humans are typically aware of team members' intention to keep or relinquish their turn before a turn switch, where the responsibility of working on a shared task is shifted. Future co-robots are also expected to provide such competence. To that end, this paper proposes the Cognitive Turn-taking Model (CTTM), which leverages cognitive models (i.e., Spiking Neural Network) to achieve early turn-taking prediction. The CTTM framework can process multimodal human communication cues (both implicit and explicit) and predict human turn-taking intentions in an early stage. The proposed framework is tested on a simulated surgical procedure, where a robotic scrub nurse predicts the surgeon's turn-taking intention. It was found that the proposed CTTM framework outperforms the state-of-the-art turn-taking prediction algorithms by a large margin. It also outperforms humans when presented with partial observations of communication cues (i.e., less than 40 of full actions). This early prediction capability enables robots to initiate turn-taking actions at an early stage, which facilitates collaboration and increases overall efficiency.
In conversational turn-taking analysis, linguistic cues such as pause duration @cite_16 and pitch levels @cite_0 have been found to play a key role in turn-taking regulation. The study of de Ruiter el al. @cite_6 revealed that syntax and semantics cues are also important in projecting the end of a speaker's turn. Non-verbal behaviors such as gaze and posture shifts have been investigated and found to be relevant to turn regulations @cite_7 .
@cite_0 @cite_16 @cite_7 @cite_6
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A key mechanism in the organization of turns at talk in conversation is the ability to anticipate or PROJECT the moment of completion of a current speaker's turn. Some authors suggest that this is achieved via lexicosyntactic cues, while others argue that projection is based on intonational contours. We tested these hypotheses in an on-line experiment, manipulating the presence of symbolic (lexicosyntactic) content and intonational contour of utterances recorded in natural conversations. When hearing the original recordings, subjects can anticipate turn endings with the same degree of accuracy attested in real conversation. With intonational contour entirely removed (leaving intact words and syntax, with a completely flat pitch), there is no change in subjects' accuracy of end-of-turn projection. But in the opposite case (with original intonational contour intact, but with no recognizable words), subjects' performance deteriorates significantly. These results establish that the symbolic (i.e. lexicosyntactic) content of an utterance is necessary (and possibly sufficient) for projecting the moment of its completion, and thus for regulating conversational turn-taking. By contrast, and perhaps surprisingly, intonational contour is neither necessary nor sufficient for end-of-turn projection. Nonverbal communication cues play an important role in human-human interaction and are expected to take a similar role in human-robot collaboration. In current industrial practice, human-robot turn-taking is explicitly human controlled, via a command channel such as switch or button. However, such a master-slave approach does not permit collaborative interaction, and requires the human to focus on both controlling the robot's behavior and on the task, thereby affecting overall performance. In this paper, implicit, nonverbal communication cues are examined as a non-explicit communication channel during a turn-taking task context. The aim of this study is to characterize the types and frequencies of nonverbal cues important to regulating turn taking during an assembly-task-type collaboration. This analysis will guide the selection of cues that can be expressed by the robot as implicit user inputs while human and robot complete a shared task. The old Asian legend about the blind men and the elephant comes to mind when looking at how different authors of scientific papers describe a piece of related prior work. It turns out that different citations to the same paper often focus on different aspects of that paper and that neither provides a full description of its full set of contributions. In this article, we will describe our investigation of this phenomenon. We studied citation summaries in the context of research papers in the biomedical domain. A citation summary is the set of citing sentences for a given article and can be used as a surrogate for the actual article in a variety of scenarios. It contains information that was deemed by peers to be important. Our study shows that citation summaries overlap to some extent with the abstracts of the papers and that they also differ from them in that they focus on different aspects of these papers than do the abstracts. In addition to this, co-cited articles (which are pairs of articles cited by another article) tend to be similar. We show results based on a lexical similarity metric called cohesion to justify our claims. © 2008 Wiley Periodicals, Inc. Deciding when to take (or not to take) the turn in a conversation is an important task. It has been stressed in the descriptive literature that such decisions must involve prediction ,a s they often seem to be made before a transition place has been reached. In computational systems, however, turn-taking is normally a reaction to parameters like pause length. In this paper, we report on experiments that try to bridge this gap. We describe an experiment (using controlled stimuli) that shows human performance at prediction of turn-taking decisions and then show that a model automatically induced from data can reach a similar level of performance. We then describe a series of experiments on spontaneous dialogue data where we combine pause thresholds with syntactic and prosodic information to make turn-taking decisions, successively reducing the pause threshold until reaction becomes prediction. All our classifiers improve significantly over the baselines; prediction however is shown to be the hardest task, and we discuss additional information sources that could improve it. Index Terms: turn-taking, machine learning, prosody. Preface 1. Introduction 2. Some context for Context Analysis: a view of the origins of structural studies of face-to-face interaction 3. Some functions of gaze direction in two-person conversation 4. Movement co-ordination in social interaction 5. Some functions of the face in a kissing round 6. A description of some human greetings 7. Spatial organisation in social encounters: the F-formation system 8. Behavioural foundations for the process of frame-attunement in face-to-face interaction List of films cited References Index. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . This paper describes a novel algorithm to dynamically set endpointing thresholds based on a rich set of dialogue features to detect the end of user utterances in a dialogue system. By analyzing the relationship between silences in user's speech to a spoken dialogue system and a wide range of automatically extracted features from discourse, semantics, prosody, timing and speaker characteristics, we found that all features correlate with pause duration and with whether a silence indicates the end of the turn, with semantics and timing being the most informative. Based on these features, the proposed method reduces latency by up to 24 over a fixed threshold baseline. Offline evaluation results were confirmed by implementing the proposed algorithm in the Let's Go system. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. We analyze humorous spoken conversations from a classic comedy television show, FRIENDS, by examining acoustic-prosodic and linguistic features and their utility in automatic humor recognition. Using a simple annotation scheme, we automatically label speaker turns in our corpus that are followed by laughs as humorous and the rest as non-humorous. Our humor-prosody analysis reveals significant differences in prosodic characteristics (such as pitch, tempo, energy etc.) of humorous and non-humorous speech, even when accounted for the gender and speaker differences. Humor recognition was carried out using standard supervised learning classifiers, and shows promising results significantly above the baseline. A new form of document coupling called co-citation is defined as the frequency with which two documents are cited together. The co-citation frequency of two scientific papers can be determined by comparing lists of citing documents in the Science Citation Index and counting identical entries. Networks of co-cited papers can be generated for specific scientific specialties, and an example is drawn from the literature of particle physics. Co-citation patterns are found to differ significantly from bibliographic coupling patterns, but to agree generally with patterns of direct citation. Clusters of co-cited papers provide a new way to study the specialty structure of science. They may provide a new approach to indexing and to the creation of SDI profiles. This paper reports a systematic review of empirical studies concerning the effectiveness of elicitation techniques, and the subsequent aggregation of empirical evidence gathered from those studies. The most significant results of the aggregation process are as follows: (1) Interviews, preferentially structured, appear to be one of the most effective elicitation techniques; (2) Many techniques often cited in the literature, like card sorting, ranking or thinking aloud, tend to be less effective than interviews; (3) Analyst experience does not appear to be a relevant factor; and (4) The studies conducted have not found the use of intermediate representations during elicitation to have significant positive effects. It should be noted that, as a general rule, the studies from which these results were aggregated have not been replicated, and therefore the above claims cannot be said to be absolutely certain. However, they can be used by researchers as pieces of knowledge to be further investigated and by practitioners in development projects, always taking into account that they are preliminary findings. This paper reports on the development of a multi-agent simulation of small group discussion that focusses on the interaction and the coordination of turn-taking. We describe the addition of nonverbal behaviours, such as gaze, gestures, posture shifts and head and facial expression, to the model; how the agents in the simulation take these behaviours into account in their decisions to speak, to stop, or to give feedback. The simulation is to be evaluated comparing its statistical profile against the statistics generated by a simpler, base model, one without the nonverbal behaviours, to show that it better approximates the statistics of a real group discussion. The properties to be assessed include mean transition intervals, turn lengths, relation of gaze to speaking order, frequency of simultaneous starts, and of feedback. Cognitive neuroscience experiments show how people intensify the exchange of non-verbal cues when they work on a joint task towards a common goal. When individuals share their intentions, it creates a social interaction that drives the mutual alignment of their actions and behavior. To understand the intentions of others, we strongly rely on the gaze cues. According to the role each person plays in the interaction, the resulting alignment of the body and gaze movements will be different. This mechanism is key to understand and model dyadic social interactions. Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance. Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... Scholars are increasingly citing electronic “web references” which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To “webcite” a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its “instructions for authors” accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) “prospectively” before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted “citing articles” (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues are addressed by honouring respective Internet standards (robot exclusion files, no-cache and no-archive tags). Long-term preservation is ensured by agreements with libraries and digital preservation organizations. The resulting WebCite Index may also have applications for research assessment exercises, being able to measure the impact of Web services and published Web documents through access and Web citation metrics. @PARASPLIT [J Med Internet Res 2005;7(5):e60] In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. This article reports the findings of a single study examining irony in talk among friends. Sixty-two 10-min conversations between college students and their friends were recorded and analyzed. Five main types of irony were found: jocularity, sarcasm, hyperbole, rhetorical questions, and understatements. These different forms of ironic language were part of 8 of all conversational turns. Analysis of these utterances revealed varying linguistic and social patterns and suggested several constraints on how and why people achieve ironic meaning. The implications of this conclusion for psychological theories of irony are discussed. Spoken language understanding (SLU) is an essential component in conversational systems. Most SLU component treats each utterance independently, and then the following components aggregate the multi-turn information in the separate phases. In order to avoid error propagation and effectively utilize contexts, prior work leveraged history for contextual SLU. However, the previous model only paid attention to the content in history utterances without considering their temporal information and speaker roles. In the dialogues, the most recent utterances should be more important than the least recent ones. Furthermore, users usually pay attention to 1) self history for reasoning and 2) others' utterances for listening, the speaker of the utterances may provides informative cues to help understanding. Therefore, this paper proposes an attention-based network that additionally leverages temporal information and speaker role for better SLU, where the attention to contexts and speaker roles can be automatically learned in an end-to-end manner. The experiments on the benchmark Dialogue State Tracking Challenge 4 (DSTC4) dataset show that the time-aware dynamic role attention networks significantly improve the understanding performance. We present a study of anonymized data capturing a month of high-level communication activities within the whole of the Microsoft Messenger instant-messaging system. We examine characteristics and patterns that emerge from the collective dynamics of large numbers of people, rather than the actions and characteristics of individuals. The dataset contains summary properties of 30 billion conversations among 240 million people. From the data, we construct a communication graph with 180 million nodes and 1.3 billion undirected edges, creating the largest social network constructed and analyzed to date. We report on multiple aspects of the dataset and synthesized graph. We find that the graph is well-connected and robust to node removal. We investigate on a planetary-scale the oft-cited report that people are separated by "six degrees of separation" and find that the average path length among Messenger users is 6.6. We find that people tend to communicate more with each other when they have similar age, language, and location, and that cross-gender conversations are both more frequent and of longer duration than conversations with the same gender.