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Comunicació presentada a la 2016 Conference of the North American Chapter of the Association for Computational Linguistics, celebrada a San Diego (CA, EUA) els dies 12 a 17 de juny 2016. ; We introduce recurrent neural network grammars,/nprobabilistic models of sentences with/nexplicit phrase structure. We explain efficient/ninference procedures that allow application to/nboth parsing and language modeling. Experiments/nshow that they provide better parsing in/nEnglish than any single previously published/nsupervised generative model and better language/nmodeling than state-of-the-art sequential/nRNNs in English and Chinese. ; This work was sponsored in part by the Defense/nAdvanced Research Projects Agency (DARPA)/nInformation Innovation Office (I2O) under the/nLow Resource Languages for Emergent Incidents/n(LORELEI) program issued by DARPA/I2O under/nContract No. HR0011-15-C-0114; it was also supported/nin part by Contract No. W911NF-15-1-0543/nwith the DARPA and the Army Research Office/n(ARO). Approved for public release, distribution/nunlimited. The views expressed are those of the authors/nand do not reflect the official policy or position/nof the Department of Defense or the U.S. Government./nMiguel Ballesteros was supported by the/nEuropean Commission under the contract numbers/nFP7-ICT-610411 (project MULTISENSOR) and/nH2020-RIA-645012 (project KRISTINA).

Early in the COVID-19 pandemic there was widespread concern that healthcare systems would be overwhelmed, and specifically, that there would be insufficient critical care capacity in terms of beds, ventilators or staff to care for patients. In the UK, this was avoided by a threefold approach involving widespread, rapid expansion of critical care capacity, reduction of healthcare demand from non-COVID-19 sources by temporarily pausing much of normal healthcare delivery, and by governmental and societal responses that reduced demand through national lockdown. Despite high-level documents designed to help manage limited critical care capacity, none provided sufficient operational direction to enable use at the bedside in situations requiring triage. We present and describe the development of a structured process for fair allocation of critical care resources in the setting of insufficient capacity. The document combines a wide variety of factors known to impact on outcome from critical illness, integrated with broad-based clinical judgement to enable structured, explicit, transparent decision-making founded on robust ethical principles. It aims to improve communication and allocate resources fairly, while avoiding triage decisions based on a single disease, comorbidity, patient age or degree of frailty. It is designed to support and document decision-making. The document has not been needed to date, nor adopted as hospital policy. However, as the pandemic evolves, the resumption of necessary non-COVID-19 healthcare and economic activity mean capacity issues and the potential need for triage may yet return. The document is presented as a starting point for stakeholder feedback and discussion.