Improving Machine Translation with Phrase Pair Injection and Corpus Filtering (2022)

Abstract: In this paper, we show that the combination of Phrase Pair Injection and Corpus Filtering boosts the performance of Neural Machine Translation (NMT) systems. We extract parallel phrases and sentences from the pseudo-parallel corpus and augment it with the parallel corpus to train the NMT models. With the proposed approach, we observe an improvement in the Machine Translation (MT) system for 3 low-resource language pairs, Hindi-Marathi, English-Marathi, and English-Pashto, and 6 translation directions by up to 2.7 BLEU points, on the FLORES test data. These BLEU score improvements are over the models trained using the whole pseudo-parallel corpus augmented with the parallel corpus.

## introduction deep neural architectures are data hungry. it is believed that the robustness of an nmt model depends on the size of the training corpus. however, not all language pairs have a substantial amount of parallel data. the primary data resource for low-resource languages is the web. however, the web-crawled data contains a lot of noise that degrades the performance of nmt systems. hence, the quality of the training data is as important as its quantity. we aim to improve the quality of machine translation for hindi-marathi, english-marathi, and english-pashto language pairs by using the labse-based @xcite ) corpus filtering along with the phrase pair injection. we use phrase pair injection to increase the size and labse-based corpus filtering to improve the quality of the parallel data extracted from the pseudoparallel corpus. we observe that using these two techniques together makes the optimum use of the pseudo-parallel corpus. the contributions of this work are as follows: ## related work neural networks have become very popular with increased computational power in recent times. the transformer model introduced by @xcite gave significant improvements in the quality of translation as compared to the previous approaches @xcite . transformers allow parallelization, which enables the model to train faster and get better performances. there are several prior studies to improve the quality and size of the parallel corpus. a set of heuristic rules were proposed by @xcite to remove low-quality sentence pairs from the noisy parallel corpus. another popular approach is to compute cosine similarities between the source and target sentence embeddings. feng et al. (2020) @xcite augmented the raw phrase table with the parallel corpus. the raw phrase table contains very noisy and repetitive phrase pairs. we observe that augmenting the whole phrase table with parallel corpus does not show much improvement in the performance of the mt system. in contrast, we first extract the longest unique phrase pairs from the phrase table and then further filter them using labse filtering to extract good quality phrase pairs. then we augment these good quality phrase pairs with the labse filtered parallel sentences. this helps improve the performance of mt systems significantly. ## approaches we first discuss a method to extract good-quality parallel sentences from the pseudo-parallel corpus. then we discuss a method to extract good quality phrase pairs from the pseudo-parallel corpus. ## experimental setup in this section, we discuss the setup of various experiments that we performed. we use byte pair encoding @xcite as a segmentation technique to split words into subwords. we use 16000 merge operations for all experiments. we use the opennmt-py foot_1 @xcite library to train the transformer based nmt models. we also train the pbsmt systems using moses @xcite . then, we perform tuning using minimum error rate training (mert) to find the optimal weights which maximize the translation performance. we use a tune set of the first 2000 parallel sentences from the pseudo-parallel corpus for the mert tuning. then, we extract the ## results and analysis we evaluate our nmt models using bleu @xcite and chrf score metric. we use sacrebleu @xcite python library to calculate the bleu and chrf scores. the results of all the experiments are summarized in we can observe from the results that the models trained using the extracted phrase pairs and parallel sentences outperform the models trained using the whole pseudo-parallel corpus when augmented with the parallel corpus. the results of hindi-marathi, english-marathi and english-pashto mt models show that baseline + labse + ppi-labse and baseline + labse + ppi, perform best amongst others on wat 2021 and flores 101 test data, respectively. in hindi-marathi mt, the best models improve the performance by 0.8 and 1.1 bleu score points in @xmath0.4 and 1.7 bleu score points in @xmath1. in english-marathi mt, the best models improve the performance by 1.0 and 1.1 bleu score points in @xmath2, on the flores 101 test data. in english-pashto mt, the best models improve the performance by 2.7 and 1.0 bleu score points over the no filtering model, on the flores 101 test data. we observe that even though the corpus size of the best models is smaller than the pseudo-parallel corpus, it still performs better as it has a higher proportion of good quality parallel sentences and phrase pairs. ## conclusion and future work in this work, we show that extracting parallel sentences and phrase pairs from a pseudo-parallel corpus helps the nmt models improve its performance significantly when augmented with a parallel corpus. we show that labse filtering assists the phrase pair injection to extract the parallel data, which has good quality and quantity. in the future, we plan to use the proposed corpus filtering techniques for other language pairs. this will provide us with a general overview of how these filtering techniques perform on multiple languages. we also anticipate improvements in the result by trying different threshold values in the labse filtering and phrase pair injection.