We present the contribution of Universitat Pompeu Fabra's NLP group to the SemEval Task 9.2 (AMR-to-English Generation). The proposed generation pipeline comprises: (i) a series of rule-based graphtransducers for the syntacticization of the input graphs and the resolution of morphological agreements, and (ii) an off-theshelf statistical linearization component.

Recent advances in technology have drastically downsized costs and implementation times for genomic services. The wide availability of low-cost genomic technologies and easy access to genomic data can significantly improve healthcare productivity and efficiency, all to the benefit of social well-being in general. For example, by creating the conditions for researchers to identify the causes of multiple diseases and contributing to the development of new drugs, we can improve the quality of life and give people, as users of genomic services, the means to positively impact their health. This article describes how blockchain technology can lay the foundations of an ecosystem that encourages users to acquire and share their genomic data in full awareness without fear of being circumvented to participate in the benefits and advances in genomic research. The starting point is Genesy, an innovative blockchain platform that transcribes genomic data, thus facilitating and, at the same time safeguarding, the users of genomic services in their relationships with parties interested in accessing and using data they own, such as research centers, pharmaceutical companies, hospitals, and geneticists. This result is obtained by exploiting blockchain technology's capabilities to notarize data and prevent their unauthorized use, and at the same time to make them objects of possible transactions between different parties. Looking ahead, the Genesy model can be generalized to promote an ecosystem, and a fair market, for all types of biomedical data.

L2 learners often produce "ungrammatical" word combinations such as, e.g., *give a suggestion or *make a walk. This is because of the "collocationality" of one of their items (the base) that limits the acceptance of collocates to express a specific meaning ('perform' above). We propose an algorithm that delivers, for a given base and the intended meaning of a collocate, the actual collocate lexeme(s) (make / take above). The algorithm exploits the linear mapping between bases and collocates from examples and generates a collocation transformation matrix which is then applied to novel unseen cases. The evaluation shows a promising line of research in collocation discovery.

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Even if dyslexia is neurological in origin, certain text mod ifications could make texts more accessible for people with dyslexia. We introduce DysWebxia 2.0, a model that inte grates our findings from research conducted with this target group. It alters content and presentation of the text to make it more readable. We also present the current integrations of DysWebxia in different reading software applications.