The AFRL WMT 20 News Translation Systems (2020)

Abstract: 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. This year we largely repurpose strategies from previous years’ efforts with larger datasets and also train models with precomputed word alignments under various settings in an effort to improve translation quality.

## introduction as part of the 2020 conference on machine trans lation (wmt, 2020) newstranslation shared task, the afrl human language technology team par ticipated in the russian-english portion of the competition. we largely employed our strategies from last year including languagebased filtering of training corpora with fasttext @xcite , employing transformerbased @xcite translation models and once again ut litizing system combination to fuse outputs from opennmt @xcite , marian @xcite and @xcite systems. we also examine the effects of training marian models with externally generated word alignments as described in @xcite . ## data processing for purposes of training our systems, we use the following parallel corpora: commoncrawl @xcite , yandex 1 , un v1.0 @xcite , paracrawl 2 @xcite , wikimatrix @xcite , and backtranslated data from our wmt17 system @xcite as well as edinburgh's wmt17 system (sennrich we prepare training corpora in a similar manner described in @xcite , however this year, we utilize sentencepiece @xcite with a 46kentry vocabulary foot_0 for pro cessing subword units instead of bytepair encod ing (bpe) @xcite . ## machine translation systems this year, we focused systembuilding efforts on the opennmt, marian, and moses toolkits. while most of our experimentation builds off of previ ous years' efforts, this year we examine the effects of "guidedalignment" training with the marian toolkit in an attempt to improve translation qual ity. ## experimental results results of decoding our various mt systems on wmt test sets from 2014 through 2019 are shown in marianbase is an ensemble of 5 transformer models trained with identical hyperparameters as outlined in section 3.2, with the exception of the initial random seed and using the languageid fil tered corpus described in section 2.1. individual model weights are trained via drem (erdmann and @xcite as outlined in last year's system. marianga is an ensemble of the four guided alignment models described in section 3.2: gabaseline, gaspmfastalign, gaprocrustes fastalign and gaprocrustesmgiza. individual model weights are also trained with drem. onmtbase is the baseline system described in section 3.1 and onmttune is the system that was further finetuned on newstest 20142017; scores on those test sets are not reported due to overfitting during the fine tuning process. variations of system combinations are also re ported again with the absence of onmttune due to concerns of overfitting as newstest2016 is the test set used for tuning the system combination pro cess. combinations of only two systems resulted in a segmentation fault during processing due to fragility in the combination process. we entered system 8 as our primary submission due to its performance gain on newstest2019 in a general (nonfinetuned) setting, with the intuition that this years test set would discuss similar topics or issues as last years, while the earlier sets may be dated. in contrast, we submit system 5 as an alternative due to finetuning adapting the model to the collection of recent test sets.