This paper presents a human evaluation reproduction study regarding the data-to-text generation task. The evaluation focuses in counting the supported and contradicting facts generated by a neural data-to-text model with a macro planning stage. The model is tested generating sport summaries for the ROTOWIRE dataset. We first describe the approach to reproduction that is agreed in the context of the ReproHum project. Then, we detail the entire configuration of the original human evaluation and the adaptations that had to be made to reproduce such an evaluation. Finally, we compare the reproduction results with those reported in the paper that was taken as reference.

The opaque nature of many machine learning techniques prevents the wide adoption of powerful information processing tools for high stakes scenarios. The emerging field eXplainable Artificial Intelligence (XAI) aims at providing justifications for automatic decision-making systems in order to ensure reliability and trustworthiness in the users. For achieving this vision, we emphasize the importance of a natural language textual modality as a key component for a future intelligent interactive agent. We outline the challenges of XAI and review a set of publications that work in this direction.

We present a data resource which can be useful for research purposes on language grounding tasks in the context of geographical referring expression generation. The resource is composed of two data sets that encompass 25 different geographical descriptors and a set of associated graphical representations, drawn as polygons on a map by two groups of human subjects: teenage students and expert meteorologists.

Monitoring and analysis of complex phenomena attract the attention of both academy and industry. Dealing with data produced by complex phenomena requires the use of advance computational intelligence techniques. Namely, linguistic description of complex phenomena constitutes a mature research line. It is supported by the Computational Theory of Perceptions grounded on the Fuzzy Sets Theory. Its aim is the development of computational systems with the ability to generate vague descriptions of the world in a similar way how humans do. This is a human-centric and multi-disciplinary research work. Moreover, its success is a matter of careful design; thus, developers play a key role. The rLDCP R package was designed to facilitate the development of new applications. This demo introduces the use of rLDCP, for both beginners and advance developers, in practical use cases.