A libaba Submission to the WMT 20 Parallel Corpus Filtering Task (2020)

Abstract: This paper describes the Alibaba Machine Translation Group submissions to the WMT 2020 Shared Task on Parallel Corpus Filtering and Alignment. In the filtering task, three main methods are applied to evaluate the quality of the parallel corpus, i.e. a) Dual Bilingual GPT-2 model, b) Dual Conditional Cross-Entropy Model and c) IBM word alignment model. The scores of these models are combined by using a positive-unlabeled (PU) learning model and a brute-force search to obtain additional gains. Besides, a few simple but efficient rules are adopted to evaluate the quality and the diversity of the corpus. In the alignment-filtering task, the extraction pipeline of bilingual sentence pairs includes the following steps: bilingual lexicon mining, language identification, sentence segmentation and sentence alignment. The final result shows that, in both filtering and alignment tasks, our system significantly outperforms the LASER-based system.

## introduction the parallel corpus is an essential resource for building a high quality machine translation(mt) system. it has been shown that, the higher the corpus quality, the better the performance of a mt system @xcite . many successful machine translation systems are built on the corpus crawled from the web. in practice, this kind of parallel corpus may be very noisy. the task of parallel corpus filtering is aimed at tackling the problem of cleaning noisy parallel corpora. we form the bilingual sentences quality in the following aspects. firstly, a high-quality parallel sentence pair(also called bitext) should have the property that its target sentence precisely translates the source sentence, and vice versa. in this task, we attempt to quantify the translation accuracy (also called bilingual score) of bilingual sentence pairs. secondly, the monolingual quality of the target and source sentences of a parallel corpus should also be considered. in our system, we evaluate the monolingual quality (also called monolingual score) of a target sentence due to its importance for the mt procedure. finally, the bilingual and monolingual scores are combined to evaluate bilingual sentence pairs and filter out the ones with low quality. the paper is structured as follows. section 2 describes our methods which are used in the parallel corpus filtering. in section 3, we briefly outline the pipeline of parallel sentence extraction. section 4 specifies the experiments and results as well as the dataset for building model-based methods. conclusions are drawn in section 5. ## parallel corpus filtering methods figure 1 shows the framework of parallel corpus filtering. the raw parallel corpus is firstly filtered by heuristic rules so that the very noisy sentence pairs will be removed. then, the bilingual & monolingual models are built to score all the remaining sentence pairs. by using an ensemble model, the partial scores of each sentence pair are combined to a single quality score. ## pipeline of parallel corpus extraction bilingual lexicon extraction. in the first step, by using the word alignment model, parallel token pairs are extracted from the clean parallel corpus. specifically, after tokenization, the parallel corpus is fed to the fast align toolkit to obtain the mutual translation probabilities dictionary. we then extract the token pairs with forward and backward translation probabilities higher than 0. the bilingual lexicon (i.e., the collection of parallel token pairs) will iteratively be updated after more bitexts are mined, since the lexicon is the cornerstone of bitexts mined from aligned documents which is described below. language identification. the second step is to identify the language of each document by using a language detection tool we developed. in this way, a document pair will be discarded if its detection results do not match the expected languages. sentence segmentation. this step is to split sentences in documents with rules or models. a few rules based on end-of-sentence punctuations are used to split sentences of language pashto and language sentences english words khmer-english 270k 4.2m pashto-english 106k 1.9m khmer. for english sentence segmentation, a segmentation model is built via nltk toolkit foot_2 . ## experiments and results in this section, we specify the experimental settings and results in the corpus filtering and alignment task. ## conclusions in this paper, we present our corpus filtering system for the wmt 2020 corpus filtering task. in our system, dual bilingual gpt-2 model, dual conditional cross-entropy model and ibm word alignment model are combined to filter the noisy parallel corpus. besides, a parallel sentence pairs extraction system is built to re-align the bilingual sentences. the experiments show that, compared to the baseline system, our filtering and extraction system achieve much better results.