An untapped communicative potential. Twitter as a dialogue generator 3 mechanism in electoral campaign

El uso de Twitter como herramienta estratégica para la comunicación política se ha incrementado notablemente durante los últimos años, especialmente en período de campaña electoral. El objetivo de esta investigación es examinar el grado de cumplimiento de los principios dialógicos atribuidos a las redes sociales. Para ello se aplica la técnica del análisis de contenido cuantitativo a los tuits publicados por los principales partidos españoles (PP, PSOE, Podemos y Ciudadanos) durante las elecciones generales de 2015. Los resultados demuestran que pese al elevado número de publicaciones realizadas por los cuatro partidos, ninguno aprovecha el potencial dialógico de TwitterThe use of Twitter as strategic tool for political communication has increased considerably in recent years, particularly during electoral campaigns. The main goal of this paper is to examine the degree of compliance with the principles of dialogue attributed to social media. To achieve this, a quantitative content analysis was carried out on the tweets shared by the main Spanish political parties during the 2015 General Elections. The results show that although a high number of tweets were made by the four political parties during this period, none of them took advantage of the full potential of dialogue on TwitterEste trabajo forma parte del proyecto de investigación CSO2014-52283-C2-1-P, financiado por el Ministerio de Economía y Competitividad del Gobierno de Españ

Better Conversations by Modeling,Filtering,and Optimizing for Coherence and Diversity

We present three enhancements to existing encoder-decoder models for open-domain conversational agents, aimed at effectively modeling coherence and promoting output diversity: (1) We introduce a measure of coherence as the GloVe embedding similarity between the dialogue context and the generated response, (2) we filter our training corpora based on the measure of coherence to obtain topically coherent and lexically diverse context-response pairs, (3) we then train a response generator using a conditional variational autoencoder model that incorporates the measure of coherence as a latent variable and uses a context gate to guarantee topical consistency with the context and promote lexical diversity. Experiments on the OpenSubtitles corpus show a substantial improvement over competitive neural models in terms of BLEU score as well as metrics of coherence and diversity

Dataflow Dialogue Generation

We demonstrate task-oriented dialogue generation within the dataflow dialogue paradigm. We show an example of agenda driven dialogue generation for the MultiWOZ domain, and an example of generation without an agenda for the SMCalFlow domain, where we show an improvement in the accuracy of the translation of user requests to dataflow expressions when the generated dialogues are used to augment the translation training dataset

A grammatical specification of human-computer dialogue

The Seeheim Model of human-computer interaction partitions an interactive application into a user-interface, a dialogue controller and the application itself. One of the formal techniques of implementing the dialogue controller is based on context-free grammars and automata. In this work, we modify an off-the-shelf compiler generator (YACC) to generate the dialogue controller. The dialogue controller is then integrated into the popular X-window system, to create an interactive-application generator. The actions of the user drive the automaton, which in turn controls the application

Twitter and political communication: the case of the Partido Popular and Podemos in the 2016 general elections

Este trabajo analiza el uso que hicieron de Twitter las cinco cuentas oficiales de las comunidades autónomas con mayor número de seguidores del Partido Popular y Podemos durante la campaña electoral para las elecciones generales de 2016. Siguiendo la metodología del análisis de contenido, se analizaron 1.845 tuits. Los resultados obtenidos confirman el uso de Twitter como herramienta unidireccional, sin llegar a establecerse un diálogo fluido entre partidos y votantes. Twitter es utilizado como un medio para la difusión de información y la promoción de aspectos propios de la campaña electoral.This paper analyzes how Twitter was used by the five official accounts of the Autonomous Communities with the largest number of followers of the Partido Popular and Podemos during the electoral campaign for the 2016 general elections. Based on the content analysis methodology, 1.845 tweets were analyzed. The results obtained confirm the use of Twitter as a unidirectional tool, without establishing a fluid dialogue between parties and voters. Twitter is used to disseminate information and promote aspects of the electoral campaign

THE ROLE OF COGNITIVE APPORTIONMENT IN INFORMATION SYSTEMS

As the number of information system users increases, we are witnessing a related increase in the complexity and the diversity of their applications. The increasing functional complexity amplifies the degree of functional and technical understanding required of the user to make productive use of the application tools. Emerging technologies, increased and varied user interests and radical changes in the nature of applications give rise to the opportunity and necessity to re-examine the proper apportionment of cognitive responsibilities in human/system interaction. Examples illustrate the opportunities afforded by such an examination. A framework is presented that illustrates many of the tradeoffs that occur in a reapportionment activity. A knowledge-based architecture is proposed to facilitate both static and dynamic reapportionment decisions

A Comprehensive Review of Data-Driven Co-Speech Gesture Generation

Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co-speech gestures is a long-standing problem in computer animation and is considered an enabling technology in film, games, virtual social spaces, and for interaction with social robots. The problem is made challenging by the idiosyncratic and non-periodic nature of human co-speech gesture motion, and by the great diversity of communicative functions that gestures encompass. Gesture generation has seen surging interest recently, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep-learning-based generative models, that benefit from the growing availability of data. This review article summarizes co-speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule-based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text, and non-linguistic input. We also chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method. Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human-like motion; grounding the gesture in the co-occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.Comment: Accepted for EUROGRAPHICS 202