Improving Translation Quality for Low-Resource I nuktitut with Various Preprocessing Techniques (2023)

Abstract: Neural machine translation has been shown to outperform all other machine translation paradigms when trained in a high-resource setting. However, it still performs poorly when dealing with low-resource languages, for which parallel data for training is scarce. This is especially the case for morphologically complex languages such as Turkish, Tamil, Uyghur, etc. In this paper, we investigate various preprocessing methods for Inuktitut, a low-resource indigenous language from North America, without a morphological analyzer. On both the original and romanized scripts, we test various preprocessing techniques such as Byte-Pair Encoding, random stemming, and data augmentation using Hungarian for the Inuktitut-to-English translation task. We found that there are benefits to retaining the original script as it helps to achieve higher BLEU scores than the romanized models.

## introduction while state-of-the-art machine translation (mt) systems are achieving close to human-like translations on a restricted set of highly researched languages @xcite @xcite @xcite @xcite , they fail to obtain equally good results on languages for which there is a lack of resources @xcite . in fact, these end-to-end neural encoder-decoder mt systems are quite data hungry, requiring parallel datasets in the tens or even hundreds of millions of sentences to outperform statistical models; datasets which are only available for a few of the spoken languages of the world @xcite . the unavailability of parallel data for most world languages is only the tip of the iceberg because, even when there is data available, the data can be very domain-specific and contain a lot of noise @xcite . @xcite and @xcite provide an overview of current research methods tackling low-resource mt, by addressing different aspects and problems. the data and tools scarcity problem in nlp creates the need to simulate lowresource scenarios by taking a small sample of data from a high-resource language so that currently existing tools can be easily tested in low-resource settings @xcite . the lack of suitable preprocessing tools hinders research on these languages @xcite . when available, linguistic tools, such as morphological segmentation, are paramount for preprocessing the data and obtaining subword segmentation, to better deal with out-of-vocabulary words; the most common strategies include bpe and sentencepiece @xcite . in this paper, we tackle the issue of preprocessing and its effect on translation quality when dealing with a highly agglutinative and morphologically complex low-resource language, inuktitut. our goal is to test several preprocessing techniques to determine which yields the best mt results for inuktitut-english. we experiment with byte-pair encoding (bpe) and random stemming, on both the romanized and the original inuktitut scripts. we also incorporate hungarian data into training, to determine if additional in-domain data from another language would help increase the translation quality. ## results we use bleu @xcite the baseline model achieved a bleu score of 11.3 on the inuktitut script and 13.0 on the romanized script. the bpe-only model achieved the best bleu score of all of the romanized experiments, with a score of 20.3, but was still outperformed by the model trained on the inuktitut script, which achieved a bleu score of 20.6. the bpe + hungarian model achieved the best bleu score overall, scoring 21.0 on the inuktitut script. for both initial random stemming and iu 3 , en 5 stemming experiments, models using the inuktitut script perfigure 2: model predictions with repetitions formed best, achieving a bleu score of 14.9 and 19.4 respectively. ## discussion the overall lack of parallel data to be used during training led to a lack of varied language, resulting both in an abundance of unknown tokens and the repeated use of simplified words in final translations, as displayed in 1. for some translations, it seems as if the model is taking liberties with the original intent of the speaker. see figure 1 . there are a few cases where the translation does not match the reference sentence, but it still infers a similar meaning, for instance, by congratulating the students in the first example and linking "graduating in the future" to "can look forward to greater opportunities" in the second. for this reason, having fluent human speakers rate final translations may be helpful for future experiments to determine the semantic intent of the original inuktitut sentence and the differences in speaking these in the original language. the inclusion of the original script during training showed better results in certain contexts, which has often been ignored in other research. ## conclusion we show that using bpe and (random) stemming as preprocessing techniques improves the translation quality for inuktitut when no morphological analyzer is available for the original inuktitut script, which has not received much attention thus far. we also experiment with data augmentation using hungarian, which yielded better translation quality on the inuktitut-english translation task.