Adaptability to the audience is an important feature for conversational systems, especially in the healthcare dissemination field, where scientific concepts have to be delivered to a potentially wide range of users. This work presents an evaluation of the capability of LLMs to support style transfer according to the target user’s age group. Two complementary evaluation methods were employed: an automatic assessment based on the ARI readability index, and a human experts evaluation focusing on appropriateness depending on the user’s educational level as well as content accuracy. Results show that LLMs efficiently switch style when provided with information about the user’s age while managing content still requires the adoption of safety measures.

This paper explores the application of the Influence Diagrams model for decision-making in the context of conversational agents. The system consists of a Conversational Recommender System (CoRS), in which the decision-making module is separate from the language generation module. It provides the capability to evolve a belief based on user responses, which in turn influences the decisions made by the conversational agent. The proposed system is based on a pre-existing CoRS that relies on Bayesian Networks informing a separate decision process. The introduction of Influence Diagrams aims to integrate both Bayesian inference and the dialogue move selection phase into a single model, thereby generalising the decision-making process. To test the effectiveness and plausibility of the dialogues generated by the developed CoRS, a dialogue simulator was created and the simulated interactions were evaluated by a pool of human judges.

In dialogue analysis, characterising named entities in the domain of interest is relevant in order to understand how people are making use of them for argumentation purposes. The movie recommendation domain is a frequently considered case study for many applications and by linguistic studies and, since many different resources have been collected throughout the years to describe it, a single database combining all these data sources is a valuable asset for cross-disciplinary investigations. We propose an integrated graph-based structure of multiple resources, enriched with the results of the application of graph analytics approaches to provide an encompassing view of the domain and of the way people talk about it during the recommendation task. While we cannot distribute the final resource because of licensing issues, we share the code to assemble and process it once the reference data have been obtained from the original sources.

This paper presents a web platform with an its own graphic environment to visualize and filter multilevel phonetic annotations. The tool accepts as input Annotation Graph XML and Praat TextGrids files and converts these files into a specific XML format. XML output is used to browse data by means of a web tool using a visualization metaphor, namely a timeline. A timeline is a graphical representation of a period of time, on which relevant events are marked. Events are usually distributed over many layers in a geometrical metaphor represented by segments and points spatially distributed with reference to a temporal axis. The tool shows all the annotations included in the uploaded dataset, allowing the listening of the entire file or of its parts. Filtering is allowed on annotation labels by means of string pattern matching. The web service includes cloud services to share data with other users. The tool is available at http://w-phamt.fisica.unina.it

Prosodic research in recent years has been supported by a number of automatic analysis tools aimed at simplifying the work that is requested to study intonation. The need to analyze large amounts of data and to inspect phenomena that are often ambiguous and difficult to model makes the prosodic research area an ideal application field for computer based processing. One of the main challenges in this field is to model the complex relations occurring between the segmental level, mainly in terms of syllable nuclei and boundaries, and the supra-segmental level, mainly in terms of tonal movements. The goal of our contribution is to provide a tool for automatic annotation of prosodic data, the Prosomarker, designed to give a visual representation of both segmental and suprasegmental events. The representation is intended to be as generic as possible to let researchers analyze specific phenomena without being limited by assumptions introduced by the annotation itself. A perceptual account of the pitch curve is provided along with an automatic segmentation of the speech signal into syllable-like segments and the tool can be used both for data exploration, in semi-automatic mode, and to process large sets of data, in automatic mode.