CUNI Systems in WMT 21: Revisiting Backtranslation Techniques for E nglish- C zech NMT (2021)

Abstract: We describe our two NMT systems submitted to the WMT2021 shared task in English-Czech news translation: CUNI-DocTransformer (document-level CUBBITT) and CUNI-Marian-Baselines. We improve the former with a better sentence-segmentation pre-processing and a post-processing for fixing errors in numbers and units. We use the latter for experiments with various backtranslation techniques.

## introduction in this paper, we describe our two nmt systems submitted to the wmt 2021 english-czech news translation shared task: "cuni-doctransformer" (charles university document-level transformer) and "cuni-marian-baselines". in addition, we submitted also "cuni-transformer2018", which is exactly the same system (sentence-level) as submitted in 2018 @xcite . cuni-doctransformer uses the same model as submitted last year @xcite , but with improved sentence segmentation (section 3.1) and number-unit postprocessing (section 3.2). this system was submitted for both @xmath0. cuni-marian-baselines is an attempt at reimplementation of the original cuni-transformer2018 in marian @xcite , where we experiment with various setups of tagged backtranslation (section 4). this system was trained only for @xmath1. according to automatic evaluation provided by the wmt organizers ( ## common settings we thus decided to first split the input segments into sentences using udpipe @xcite . unfortunately, udpipe tends to over-segment. 3 such over-segmentation may lead to serious errors in the translation, even when using the documentlevel model. we thus restrict the sentences boundaries detected by udpipe only to boundaries after sentence-final punctuation, using a simple rulebased segmenter from udapi @xcite . this improved bleu on our dev set slightly. ## experiments in marian the goals of the experiments described in this section were: ## conclusions in this paper, we presented two sets of experiments: automatic correction of numeric expressions with units in rule-based post-processing and various settings of tagged backtranslation. the correction of numeric expressions with units focuses on errors which are relatively rare and do not affect automatic metrics such as bleu much, but can result in serious misunderstanding of the meaning of the translation. unfortunately, these errors won't be properly reflected even in the official wmt (context-sensitive, but sentence-level) manual evaluation, where each sentence's score is weighted the same, even if some errors are crucial for the meaning of the whole document. the experiments with tagged backtranslation using a marian reimplementation of cubbitt did not show any substantial differences in the results nor any consistent pattern. however, we hope that future work continuing the research on various types of training data (authentic vs. synthetic; forward vs backward; different domains) and their synergies may bring new results and better understanding of the backtranslation training etc. figure 4: wmt16 bleu training curves of nonaveraged models