no code implementations • WMT (EMNLP) 2020 • Jeremy Gwinnup, Tim Anderson
This report summarizes the Air Force Research Laboratory (AFRL) machine translation (MT) systems submitted to the news-translation task as part of the 2020 Conference on Machine Translation (WMT20) evaluation campaign.
no code implementations • WMT (EMNLP) 2021 • Grant Erdmann, Jeremy Gwinnup, Tim Anderson
This paper describes the Air Force Research Laboratory (AFRL) machine translation sys- tems and the improvements that were developed during the WMT21 evaluation campaign.
1 code implementation • 11 Sep 2022 • Nicholas Kashani Motlagh, Jim Davis, Tim Anderson, Jeremy Gwinnup
We propose a new Reject Option Classification technique to identify and remove regions of uncertainty in the decision space for a given neural classifier and dataset.
no code implementations • WS 2020 • Brian Ore, Eric Hansen, Tim Anderson, Jeremy Gwinnup
This report summarizes the Air Force Research Laboratory (AFRL) submission to the offline spoken language translation (SLT) task as part of the IWSLT 2020 evaluation campaign.
1 code implementation • 28 Aug 2019 • Gregory Castanon, Nathan Shnidman, Tim Anderson, Jeffrey Byrne
The Out the Window (OTW) dataset is a crowdsourced activity dataset containing 5, 668 instances of 17 activities from the NIST Activities in Extended Video (ActEV) challenge.
no code implementations • WS 2019 • Jeremy Gwinnup, Grant Erdmann, Tim Anderson
This paper describes the Air Force Research Laboratory (AFRL) machine translation systems and the improvements that were developed during the WMT19 evaluation campaign.
no code implementations • WS 2018 • Jeremy Gwinnup, Tim Anderson, Grant Erdmann, Katherine Young
This paper describes the Air Force Research Laboratory (AFRL) machine translation systems and the improvements that were developed during the WMT18 evaluation campaign.
1 code implementation • WS 2018 • Brian Thompson, Huda Khayrallah, Antonios Anastasopoulos, Arya D. McCarthy, Kevin Duh, Rebecca Marvin, Paul McNamee, Jeremy Gwinnup, Tim Anderson, Philipp Koehn
To better understand the effectiveness of continued training, we analyze the major components of a neural machine translation system (the encoder, decoder, and each embedding space) and consider each component's contribution to, and capacity for, domain adaptation.