Iterative Back Translation Revisited: An Experimental Investigation for Low-resource E nglish A ssamese Neural Machine Translation (2023)

Abstract: Back Translation has been an effective strategy to leverage monolingual data both on the source and target sides. Research have opened up several ways to improvise the procedure, one among them is iterative back translation where the monolingual data is repeatedly translated and used for re-training for the model enhancement. Despite its success, iterative back translation remains relatively unexplored in low-resource scenarios, particularly for rich Indic languages. This paper presents a comprehensive investigation into the application of iterative back translation to the low-resource English-Assamese language pair. A simplified version of iterative back translation is presented. This study explores various critical aspects associated with back translation, including the balance between original and synthetic data and the refinement of the target (backward) model through cleaner data retraining. The experimental results demonstrate significant improvements in translation quality. Specifically, the simplistic approach to iterative back translation yields a noteworthy +6.38 BLEU score improvement for the EnglishAssamese translation direction and a +4.38 BLEU score improvement for the AssameseEnglish translation direction. Further enhancements are further noticed when incorporating higher-quality, cleaner data for model retraining highlighting the potential of iterative back translation as a valuable tool for enhancing low-resource neural machine translation (NMT).

## introduction low resource neural machine translation (nmt) is an open problem where even the most successful state-of-the-art methods or models have a limited impact and adapting nmt systems under these restrictions is still a challenging task. the basic requirement for any popular neural model is an enormous amount of data which becomes an inher-ent limitation for low-resource languages where only tens of thousands of parallel data is available. although there is a dearth of parallel data, monolingual data is abundantly available for most of the cases. a well-known, simple and effective method is back-translation (bt) where a target to source nmt model is trained on the parallel data and is used to translate the monolingual target data to generate a relatively large pseudoparallel dataset. the pseudo-parallel dataset can thus be utilised to appease the data hungry nmt models which appears to be a suitable method for low resource scenarios. @xcite have demonstrated successful implementation of the bt approach. simple enough as it seems to be, several factors such as the data quality @xcite , ratio of original to synthetic data size @xcite , model tuning on clean data, impact of merging the original parallel data to the synthetic parallel data @xcite , noise induction @xcite etc influence a bt model. we investigate back translation for low-resource language pair: english-assamese. as pointed out by @xcite , model performance is negatively affected by the back translated data which is error-prone in comparison to the 'perfect' human translations. to mitigate this effect of bt and in order to enhance the translation quality we adopt bt in an iterative manner, inspired by @xcite , hypothyzing to produce better translations with every iteration. contributions of this paper: ## related work the whole idea of back translation in low resource scenario is to augment the limited data. therefore, extensive research has been conducted in this direction to achieve improved results by investigating in the directions described in the following subsection. ## methododology, experiments and results all experiments are carried out using the pytorch version of the open-source nmt toolkit known as opennmt-py foot_0 on a system equipped with a 256gb ssd and an intel® xeon(r) gold 6230r processor, accompanied by an nvidia rtx a6000 with 48 gb of gpu ram. due to the limited availability of a single extensive data source, the genuine parallel training data comprises a combination of four distinct datasets: national platform for language technology foot_1 (nplt), samanantar @xcite , pmindia (pmi) @xcite , and an in-house parallel data collection known as machine aided translation (mat). the individual contribution of each data source is detailed in to test the performance of the models developed, a comprehensive test set is designed to include complexity in all levels, be it domain-wise or of various sentence lengths. it is developed by linguistic experts comprising of seven (7) domains which includes administration, agriculture, education, judiciary, health, tourism and technology and climate (combined). every domain also holds sentences with four different sentence length buckets namely, (1-10), (11-20), (21-30) and 30+. ## conclusion from the experiments conducted we find that iterative back translation stands out as an effective approach, particularly for low-resource nmt scenarios. secondly, it is evident that the utilization of higher-quality synthetic data significantly enhances model performance in both directions, underscoring the critical importance of data quality in nmt training. and finally, our analysis reveals no distinct, consistent trend on the performance of back translation models concerning the proportional inclusion of synthetic data alongside the original parallel dataset. however, it is important to acknowledge that these trends cannot be generalized as every language has their own peculiarities which makes them distinct from one another. we plan to further investigate these directions in a bunch of low-resource language pairs to arrive at a general conclusion.