Innovation Networks in the Learning Economy

This paper presents breakthroughs of the proposal for a methodology to develop innovation networks with virtual links. It considers stages of analysis, design, implementation and follow up and can be applied to both large companies and SMEs. Fragmented approaches have predominance in literature, for this reason we want to close that gap somehow, within the framework of a systemic, dynamic, organic, and transparent approach. The methodology values the already existing contributions, from which new elements have been added, specially the support of electronic networks (ICT). We consider that innovation in networks must transcend spatial frontiers, thus considering virtual links since they turn the organizations faster and more flexible, therefore facilitating a more efficient access to information and knowledge; considered the key aspects in today’s interactive innovation process. The research methodology was bibliographical, documental, and exploratory.

Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking

The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolutional neural network structure which can be trained on dialogue act-utterance pairs without any semantic alignments or predefined grammar trees. Objective metrics suggest that this new model outperforms previous methods under the same experimental conditions. Results of an evaluation by human judges indicate that it produces not only high quality but linguistically varied utterances which are preferred compared to n-gram and rule-based systems.Comment: To be appear in SigDial 201