The Narrator: NLG for digital storytelling

We present the Narrator, an NLG component used for the generation of narratives in a digital storytelling system. We describe how the Narrator works and show some examples of generated stories

The virtual guide: a direction giving embodied conversational agent

We present the Virtual Guide, an embodied conversational agent that can give directions in a 3D virtual environment. We discuss how dialogue management, language generation and the generation of appropriate gestures are carried out in our system

A unified representation for morphological, syntactic, semantic, and referential annotations

This paper reports on the SYN-RA (SYNtax-based Reference Annotation) project, an on-going project of annotating German newspaper texts with referential relations. The project has developed an inventory of anaphoric and coreference relations for German in the context of a unified, XML-based annotation scheme for combining morphological, syntactic, semantic, and anaphoric information. The paper discusses how this unified annotation scheme relates to other formats currently discussed in the literature, in particular the annotation graph model of Bird and Liberman (2001) and the pie-in-thesky scheme for semantic annotation

A multimodal interaction system for navigation

To help users find their way in a virtual theatre we developed a navigation agent. In natural language dialogue the agent assists users looking for the location of an object or room, and it shows routes between locations. The speech-based dialogue system allows users to ask questions such as “Where is the coffee bar?” and “How do I get to the great hall?” The agent has a map and can mark locations and routes; users can click on locations and ask questions about them

A Review of the Repeated Name Penalty: Implications for Null Subject Languages

This is a critical review of the anaphoric processing delay known as the Repeated Name Penalty (RNP: Gordon, Grosz, & Gilliom, 1993). In this paper I argue that the RNP should be understood as an interaction effect between the anaphor type and the discourse prominence of the referent, and not merely as a pairwise comparison between sentences with repeated names and corresponding sentences with pronouns. I further propose that in null subject languages, the relevant anaphor that should be contrasted with the repeated name is the null pronoun because this type of pronoun represents the least informative anaphor available.Esta é uma revisão crítica do atraso de processamento conhecido como Penalidade do Nome Repetido (PNR: Gordon, Grosz e Gilliom, 1993). Neste artigo, defendo que a PNR deve ser entendida como um efeito da interação entre o tipo de anáfora e a saliência do referente discursivo, e não apenas como uma comparação pareada entre sentenças com nomes repetidos e sentenças correspondentes com pronomes. Proponho também que, em línguas com sujeito nulo, a anáfora relevante que deve ser contrastada com o nome repetido é o pronome nulo, porque esse tipo de pronome representa a anáfora menos informativa disponível.Fil: Gelormini Lezama, Carlos. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentin

Fighting with the Sparsity of Synonymy Dictionaries

Graph-based synset induction methods, such as MaxMax and Watset, induce synsets by performing a global clustering of a synonymy graph. However, such methods are sensitive to the structure of the input synonymy graph: sparseness of the input dictionary can substantially reduce the quality of the extracted synsets. In this paper, we propose two different approaches designed to alleviate the incompleteness of the input dictionaries. The first one performs a pre-processing of the graph by adding missing edges, while the second one performs a post-processing by merging similar synset clusters. We evaluate these approaches on two datasets for the Russian language and discuss their impact on the performance of synset induction methods. Finally, we perform an extensive error analysis of each approach and discuss prominent alternative methods for coping with the problem of the sparsity of the synonymy dictionaries.Comment: In Proceedings of the 6th Conference on Analysis of Images, Social Networks, and Texts (AIST'2017): Springer Lecture Notes in Computer Science (LNCS