Search Results for author: Tim Anderson

Found 10 papers, 3 papers with code

The AFRL WMT20 News Translation Systems

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.

Machine Translation Translation

Tune in: The AFRL WMT21 News-Translation Systems

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.

Machine Translation Translation

Learning When to Say "I Don't Know"

1 code implementation11 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.

text-classification Text Classification

The AFRL IWSLT 2020 Systems: Work-From-Home Edition

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.

Action Detection Activity Detection +9

Out the Window: A Crowd-Sourced Dataset for Activity Classification in Security Video

1 code implementation28 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.

General Classification

The AFRL WMT19 Systems: Old Favorites and New Tricks

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.

Domain Adaptation Machine Translation +1

The AFRL WMT18 Systems: Ensembling, Continuation and Combination

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.

Machine Translation Translation

Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation

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.

Domain Adaptation Machine Translation +1

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