In contrast to the matching paradigm, the probabilistic nature of generative rankers readily offers a fine-grained measure of uncertainty.
In any ranking system, the retrieval model outputs a single score for a document based on its belief on how relevant it is to a given search query.
no code implementations • 12 Feb 2021 • Daniel Suo, Cyril Zhang, Paula Gradu, Udaya Ghai, Xinyi Chen, Edgar Minasyan, Naman Agarwal, Karan Singh, Julienne LaChance, Tom Zajdel, Manuel Schottdorf, Daniel Cohen, Elad Hazan
We consider the problem of controlling an invasive mechanical ventilator for pressure-controlled ventilation: a controller must let air in and out of a sedated patient's lungs according to a trajectory of airway pressures specified by a clinician.
When performing cross-language information retrieval (CLIR) for lower-resourced languages, a common approach is to retrieve over the output of machine translation (MT).
We use an adversarial discriminator and train our neural ranking model on a small set of domains.
Recent work in Information Retrieval (IR) using Deep Learning models has yielded state of the art results on a variety of IR tasks.