Tune in: The AFRL WMT 21 News-Translation Systems (2021)

Abstract: This paper describes the Air Force Research Laboratory (AFRL) machine translation sys- tems and the improvements that were developed during the WMT21 evaluation campaign. This year, we explore various methods of adapting our baseline models from WMT20 and again measure improvements in performance on the Russian–English language pair.

## introduction as part of the 2021 conference on machine translation (wmt, 2021) news-translation shared task, the afrl human language technology team participated in the russian-english portion of the competition. we experiment with opennmt-tf foot_0 @xcite and marian foot_1 @xcite transformer @xcite models trained as part of our wmt20 @xcite efforts and apply varying continuedtraining and fine-tuning approaches @xcite , including a new method to select a fine-tuning set from a separate, larger corpus not used in training. we submit an opennmt-based transformer system fine-tuned on newstest test sets from 2014-2017 as our primary entry, and a marian-based transformer system fine-tuned on newstest test sets from 2014-2018 as a contrast. ## data and preprocessing since most of our efforts focus on fine-tuning existing models this year, we reuse the training corpus from our wmt20 systems which includes the following parallel corpora: commoncrawl @xcite , yandex foot_2 , un v1.0 @xcite , paracrawl foot_3 @xcite , wikimatrix @xcite , and backtranslated data from our wmt17 system @xcite as well as edinburgh's wmt17 system @xcite yielding a raw corpus of over 76.3 million lines. the new russian-english version 8 paracrawl corpus is reserved for tuning set selection as described in section 2.3. ## experimental results post evaluation a model with the opennmt-tf configuration described in section 3.1 was trained on all the unfiltered data (approx. 76m million bi-text). the results are shown in ## conclusion while our two submission systems employ a standard method of fine-tuning to adapt models towards a test set, we find that our methods to sample a similarly-sized tuning corpus from a larger body of text while only using information about the source side of that data yields a reasonable improvement in translation quality. such a technique could be useful in adapting translation models to specific domains where only the source language of a text source is available. using actual in-domain data, such as the provided news development sets, for fine-tuning provide a substantial gain in translation quality. such data is not always available and thus other selection techniques as described in section 2.3 come into play. future work will investigate combining the two approaches to see if additional gains can be obtained. the authors would like to thank emily conway and braeden bowen for their assistance in human evaluation of mt output.