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).