In this paper we describe the contributions made by the European H2020 project “Prêt-à-LLOD” (‘Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors’) to the further development of the Linguistic Linked Open Data (LLOD) infrastructure. Prêt-à-LLOD aims to develop a new methodology for building data value chains applicable to a wide range of sectors and applications and based around language resources and language technologies that can be integrated by means of semantic technologies. We describe the methods implemented for increasing the number of language data sets in the LLOD. We also present the approach for ensuring interoperability and for porting LLOD data sets and services to other infrastructures, as well as the contribution of the projects to existing standards.

Statistical user simulation is an efficient and effective way to train and evaluate the performance of a (spoken) dialog system.\nIn this paper, we design and evaluate a modular data-driven dialog simulator where we decouple the “intentional” component\nof the User Simulator from the Error Simulator representing different types of ASR/SLU noisy channel distortion. While the\nformer is composed by a Dialog Act Model, a Concept Model and a User Model, the latter is centered around an Error Model. We test different Dialog Act Models and Error Models against a baseline dialog manager and compare results with real dialogs obtained using the same dialog manager. On the grounds of dialog act, task and concept accuracy, our results show that 1) datadriven\nDialog Act Models achieve good accuracy with respect to real user behavior and 2) data-driven Error Models make task\ncompletion times and rates closer to real data.

In this paper, we study how the performance and usability of web dialogue systems could be enhanced by using an appropriate representation of the different types of knowledge involved in communication: general dialogue mechanisms, specific domain-restricted linguistic and conceptual knowledge and information on how well the communication process is doing. We describe the experiments carried out to analyze how to improve this knowledge representation in the web dialogue system we developed.

In this paper we propose the integration of intelligent components technologies (natural language and discourse management) in voice web interfaces to make them smarter. We describe how we have integrated reusable components of dialogue management and language processing in a multilingual voice system to improve its friendliness and portability. The dialogue management component deals with complex dialogue phenomena, such as user-initiative dialogues, and follows the information state-based theory. The resulting dialogue system supports friendly communication (through the telephone and the web) in several languages: English, Spanish, Catalan and Italian. The dialogue system has been adapted to guide the users to access online public administration services.

In this paper, we describe how domain ontologies are used in a dialogue system guiding the user to access web public administration contents. The current implementation of the system supports speech (through the telephone) and text mode in different languages (English, Spanish, Catalan and Italian).