Volctrans Parallel Corpus Filtering System for WMT 2020 (2020)

Abstract: In this paper, we describe our submissions to the WMT20 shared task on parallel corpus filtering and alignment for low-resource conditions. The task requires the participants to align potential parallel sentence pairs out of the given document pairs, and score them so that low-quality pairs can be filtered. Our system, Volctrans, is made of two modules, i.e., a mining module and a scoring module. Based on the word alignment model, the mining mod- ule adopts an iterative mining strategy to extract latent parallel sentences. In the scoring module, an XLM-based scorer provides scores, followed by reranking mechanisms and ensemble. Our submissions outperform the baseline by 3.x/2.x and 2.x/2.x for km-en and ps-en on From Scratch/Fine-Tune conditions.

## introduction with the rapid development of machine translation, especially neural machine translation (nmt) @xcite @xcite , parallel corpus in high quality and large quantity is in urgent demand. these parallel corpora can be used to train and build robust machine translation models. however, for some language pairs on low-resource conditions, few parallel resources are available. since it is much easier to obtain quantities of monolingual data, it may help if we can extract parallel sentences from monolingual data through alignment and filtering. the wmt19 shared task on parallel corpus filtering for low-resource conditions @xcite provides noisy parallel corpora in sinhala-english and nepali-englis crawled from the web. participants are asked to score sentence pairs so that low-quality sentences are filtered. in this year, the wmt20 shared task on parallel corpus filtering and alignment for low-resource conditions is very similar, except that the language pairs become khmer-english and pashto-english, and the provided raw data are documents in pair, which require sentence-level alignment. besides, no data in similar languages are provided for this year. the participants are required to align sentences within documents in different languages and provide a score for each sentence pair. to evaluate the quality of the extracted sentence pairs, they are subsampled to 5 million english words and used to train a neural machine translation model. finally, the bleu score of the machine translation system is used to reflect the quality of the sentence pairs. in this paper, we propose the volctrans filtering system, which consists of a mining module and a scoring module. first, the mining module extracts and aligns potential parallel sentence pairs within documents in different languages. in particular, we introduce an iterative mining strategy to boost mining performance. we keep adding newly aligned high-quality parallel sentences to train the word alignment model, which is essential for the mining module. second, the scoring module is based on xlm @xcite , and responsible for providing scores for each sentence pair. several reranking mechanisms are also used in this module. we conduct experiments to tune the hyper-parameters for the best filtering performance, and four systems are ensembled to achieve the final results. ## experiments we conduct various experiments to evaluate the performance of different models, and select the most proper hyperparameters for both khmer-english and pashto-english. note that fs and ft denote from sratch and fine-tune respectively. firstly, we conduct the experiments with both the provided aligned sentence pairs (denoted as baseline) and our mined data at the first iteration of the mining module. it shows that our system can outperform the baseline remarkably and the ensemble of four different models can further improve the performance. as ## conclusion in this paper, we present our submissions to the wmt20 shared task on parallel corpus filtering and alignment for low-resource conditions. our volctrans system consists of two modules: a) mining module is responsible for mining potential parallel sentence pairs out of the provided document pairs. word alignment model is utilized and an iterative mining strategy is further taken to boost ft α = 0, β = 0 12.17 13.19 α = 0, @xmath2, β = 0.1 12.28 13.34 α = 0.2, @xmath3, β = 0.1 12.15 13.06 α = 0, @xmath4, β = 0.1 12.20 13.38 α = 0, @xmath5, β = 0.2 12.20 13.31 the mining performance. b) scoring module aims to evaluate sentence pairs quality according to their parallelism and fluency properties, by exploiting an xlm-based scorer. we further tune the output score with different reranking mechanism, by considering language detection confidence and ngram vocabulary coverage. finally, four models are ensembled to improve the final performance. we also make some analysis through a variety of experiments.