Automatic Annotation of Direct Speech in Written French Narratives

The automatic annotation of direct speech (AADS) in written text has been often used in computational narrative understanding. Methods based on either rules or deep neural networks have been explored, in particular for English or German languages. Yet, for French, our target language, not many works exist. Our goal is to create a unified framework to design and evaluate AADS models in French. For this, we consolidated the largest-to-date French narrative dataset annotated with DS per word; we adapted various baselines for sequence labelling or from AADS in other languages; and we designed and conducted an extensive evaluation focused on generalisation. Results show that the task still requires substantial efforts and emphasise characteristics of each baseline. Although this framework could be improved, it is a step further to encourage more research on the topic.Comment: 9 pages, ACL 202

Explainability in Music Recommender Systems

The most common way to listen to recorded music nowadays is via streaming platforms which provide access to tens of millions of tracks. To assist users in effectively browsing these large catalogs, the integration of Music Recommender Systems (MRSs) has become essential. Current real-world MRSs are often quite complex and optimized for recommendation accuracy. They combine several building blocks based on collaborative filtering and content-based recommendation. This complexity can hinder the ability to explain recommendations to end users, which is particularly important for recommendations perceived as unexpected or inappropriate. While pure recommendation performance often correlates with user satisfaction, explainability has a positive impact on other factors such as trust and forgiveness, which are ultimately essential to maintain user loyalty. In this article, we discuss how explainability can be addressed in the context of MRSs. We provide perspectives on how explainability could improve music recommendation algorithms and enhance user experience. First, we review common dimensions and goals of recommenders' explainability and in general of eXplainable Artificial Intelligence (XAI), and elaborate on the extent to which these apply -- or need to be adapted -- to the specific characteristics of music consumption and recommendation. Then, we show how explainability components can be integrated within a MRS and in what form explanations can be provided. Since the evaluation of explanation quality is decoupled from pure accuracy-based evaluation criteria, we also discuss requirements and strategies for evaluating explanations of music recommendations. Finally, we describe the current challenges for introducing explainability within a large-scale industrial music recommender system and provide research perspectives.Comment: To appear in AI Magazine, Special Topic on Recommender Systems 202

Azo-polymers modified with nucleobases and their interactions with DNA molecules

The photo-fluidization process which is specific for azo-materials opens a new perspective for their use in the field of molecules nano manipulation at the surface of the azo polymer films. This is possible considering that in the case of the UV irradiation from a polarized laser source the azo material has an unidirectional flow. Here, we investigated the structuring phenomena occurring on the surface of the azo-polysiloxanes films modified with nucleobases, upon UV irradiation. Measurements of topography and adhesive forces between polymeric substrates and a hydrophilic probe have been done by atomic force microscopy (AFM). The response of the material upon irradiation has been investigated also by using UV-VIS spectroscopy. This method allowed us to draw the photo-isomerization and relaxation curves. Also, preliminary tests were conducted to determine the capacity of the film surface to immobilize DNA molecules