Machine Translation Performance for Low-Resource Languages: A Systematic Literature Review

Applying convolutional attention mechanisms and Human Memory Search for effective English-Urdu translation

Machine Translation (MT) has recently been strengthened by the emergence of deep neural network techniques, which have brought the dream of language translation in community-based practices and clinical practices closer to reality. This chapter introduces the clinical practice of MT, especially in low resource languages. The following are some of the highlights of this chapter. How MT played a crucial role in good social reasons is focused on in this chapter. The chapter starts with the history of translation technologies that have played a vital role in various crisis and relief scenarios as the Haitian earthquake in 2010 and Translators without Borders (TWB), respectively. The Standby Task Force (SBTF) deployed the technology to tackle misinformation during Coronavirus Pandemic 2019. Next, the chapter explores the importance of the MT system, where it describes MT literacy as required across society to impact the healthcare of human beings. It describes the challenges of translation for clinical texts. Some of those are highlighted as (a) Domain: characteristics of clinical text differentiated from texts with other domains, (b) code mixed low-resource languages: noisy clinical data and lower quantity of data in the digital form available for computation, (c) lack of clinical domain corpora, and (d) very rich morphological structure: e.g., procedures (e.g., hepatico-cholangio-jejuno-stomy) and chemicals (e.g., Hydro-xy-nitro-di-hydro-thy-mine). Strategies or approaches to building MT on the clinical text can be categorized as: (1) Rule-based MT (early days), (2) Statistical MT (SMT, one of the successful methods in MT history), and (3) Neural MT (NMT, recent advancements of MT). Both SMT and NMT that work on same grounds by learning from text and are the primary focus of this chapter. L earning transparency for the patient's use of clinical data is an essential component. Much of today's healthcare data is unstructured text, such as clinical text, doctor's notes, nursing notes, lab reports, test reports, and more. A clinical practitioner is faced with a language barrier for communicating with a patient to interpret patient histories, examine a clinical diagnosis, or restate the recommended treatment method and follow-up to aid comprehension. The only way a medical practitioner encourages patients to ask questions or respond to queries is by directing them to input text with the help of MT. Finally, the chapter discusses contemporary techniques with Neural Machine Translation (NMT), namely multilingual, transfer learning on clinical data.

This paper introduces current-day automated metrics for neural machine translation evaluation and discusses two ways of result evaluation, which include evaluation using specific automated metrics as well as evaluation by an expert translator. Neural machine translation systems were tested for analysis and evaluation purposes. Google Translate and DeepL Translate systems, which apply a neural network approach, are presented as candidates. The following metrics were considered for evaluation: METEOR as a traditional reference-based metric, COMET as a neural reference-based metric, and COMET-kiwi as a neural reference-free metric. It can be noted that even if these metrics are automated, there is a huge human impact in this automation. Even models with a neural network approach are trained on data provided by humans, as today it is impossible to get rid of reference translations or quality estimations made by experts. Metrics allow to understand and better investigate machine translation, its features and limitations, as well as to determine the direction of its development. As a part of the analysis, a piece of source text was selected for translation, translated into the target languages using selected neural machine translation systems to get candidate translations, and then a reference translation was specified for each of them. The results of the metrics evaluation were helpful for understanding how close machine translation is to human translation, and also helped to learn more about the current stage of machine translation systems development. Expert evaluation helped to understand how well such systems are doing in terms of translation performance.

Machine translation aims to minimize the language barrier between people of different linguistic backgrounds. Machine translation is an automatic translation technique between pairs of different languages. Machine translation using neural network or neural machine translation came into the picture due to several limitation associated with its predecessors which are rule based and statistical based models. For large data sets with a rich range of vocabulary, the neural network machine translation system provides fair translation accuracy. We have observed that there remain very few machine translators dedicated to Indian languages, especially those spoken in the North-East area. So our paper mainly focuses on implementing a neural machine translation system for the English-Assamese language. In this paper we used five different neural machine translation models for English-Assamese language pair using LSTMs and GRUs, along with an attention layer. We have made a comparison analysis based on the performance results of these models. We used BLEU Score to calculate the accuracy of these five models, thus achieving a BLEU score of 34.168%, which was the highest among the five models.

Translating culture-related content is vital for effective cross-cultural communication.However, many culture-specific items (CSIs) often lack viable translations across languages, making it challenging to collect high-quality, diverse parallel corpora with CSI annotations.This difficulty hinders the analysis of cultural awareness of machine translation (MT) systems, including traditional neural MT and the emerging MT paradigm using large language models (LLM).To address this gap, we introduce a novel parallel corpus, enriched with CSI annotations in 6 language pairs for investigating Culturally-Aware Machine Translation-CAMT. 1 Furthermore, we design two evaluation metrics to assess CSI translations, focusing on their pragmatic translation quality.Our findings show the superior ability of LLMs over neural MTs in leveraging external cultural knowledge for translating CSIs, especially those lacking translations in the target culture.

Over the past ten years, neural machine translation (NMT) has seen tremendous growth and is now entering a phase of maturity. Despite being the most popular solution for machine translation (MT), it performs sub-optimally on under-resourced language pairs due to lack of parallel corpora as compared to high-resourced language pairs. The implementation of NMT techniques for under-resourced language pairs is receiving the attention of researchers and has resulted in a significant amount of research for many under-resourced language pairs. In view of the growth of MT, this paper describes a set of practical approaches for investigating MT between Kannada and Tulu. These two languages belong to the family of Dravidian languages and are under-resourced due to lack of tools and resources particularly the parallel corpus for MT. Since there are no parallel corpora for the Kannada-Tulu language pair for MT, this work aims to construct a parallel corpus for this language pair. As manual construction of parallel corpus is laborious, data augmentation is introduced to enhance the size of the parallel corpus along with suitable preprocessing techniques. Different NMT schemes such as recurrent neural network (RNN) baseline, bidirectional recurrent neural network (BiRNN), transformer-based NMT with and without subword tokenization, and statistical machine translation (SMT) models are implemented for MT of Kannada-Tulu and Tulu-Kannada language pairs. Empirical results reveal that the impact of data augmentation increases the bilingual evaluation understudy (BLEU) score of the proposed models. Transformer-based models with subword tokenization outperformed the other models with BLEU scores 41.82 and 40.91 for Kannada-Tulu and Tulu-Kannada MT, respectively.

In the modern era, the field of Machine Translation (MT) has seen a significant shift towards Neural Machine Translation (NMT) techniques, which have surpassed traditional Statistical Machine Translation (SMT) models in terms of the quality of translation. Despite this, the efficacy of these techniques may differ based on the language combination in consideration. While SMT is somewhat more flexible in this regard, NMT often needs sizable parallel corpora to attain high translation accuracy. As a result, a benchmark system capable of offering sufficient translation for languages with limited resources, like Nepali, remains a pipe dream. This paper focuses on translating text using statistical and neural MT techniques for the under-resourced English-Nepali language pair. As a part of this system development, we built a parallel corpus of English-Nepali in the tourism domain. We explore the impact of different tokenization techniques on translation outcomes. A substantial analysis is also done for the performance of both approaches using automatic evaluation metrics, BLEU and TER. This paper aims to provide insights into the applicability of SMT and NMT for the under-resourced English-Nepali language pair in light of two popular epitomes of tokenization and to determine the most effective approach for achieving accurate translations.

This paper proposes an English-Chinese machine translation research method based on transfer learning. First, it expounds the theory of neural machine translation and transfer learning and related technologies. Neural machine translation is discussed, the advantages and disadvantages of various models are introduced, and the transformer neural machine translation model framework is selected. For low-resource Chinese-English parallel corpus and Tibetan-Chinese parallel corpus, 30 million Chinese-English parallel corpora, 100,000 Chinese-English low-resource parallel corpora, and 100,000 Tibetan-Chinese parallel corpora were used to pretrain the transformer machine translation architecture. The decoders are all composed of 6 identical hidden layers, the initialization of the model parameters is done by the transformer uniform distribution, and the model training uses Adam as the optimizer. In the model transfer part, the parameters with the better effect of the pretrained model are transferred to the low-resource Chinese-English and Tibetan-Chinese machine translation model training, so as to achieve the purpose of knowledge transfer. The results show that the model transfer learning of low-resource Chinese-English parallel corpus improves the translation system's translation by 3.97 BLEU values compared with the translation system without transfer learning at 0.34 BLEU values. Model transfer learning on low-resource Tibetan-Chinese parallel corpus increases the BLEU value by 2.64 BLEU compared to the translation system without transfer learning. The neural machine translation system that uses BPE technology for preprocessing plus model transfer learning is compared to the translation system that only performs transfer learning and shows an improved 0.26 BLEU value.It is verified that the transfer learning method proposed in this paper has a certain improvement in the effect of low-resource Chinese-English and Tibetan-Chinese neural machine translation models.

This study examines the efficacy of employing Indonesian as an intermediary language to improve the quality of translations from Javanese to Madurese through a pivot-based approach utilizing neural machine translation (NMT). The principal objective of this research is to enhance translation precision and uniformity among these low-resource languages, hence advancing machine translation models for underrepresented languages. The data collecting approach entailed extracting parallel texts from internet sources, followed by pre-processing through tokenization, normalization, and stop-word elimination algorithms. The prepared datasets were utilized to train and assess the NMT models. An intermediary phase utilizing Indonesian is implemented in the translation process to enhance the accuracy and consistency of translations between Javanese and Madurese. Parallel text corpora were created by collecting and preprocessing data, thereafter, utilized to train and assess the NMT models. The pivot-based strategy regularly surpassed direct translation regarding BLEU scores for all n-grams (BLEU-1 to BLEU-4). The enhanced BLEU ratings signify increased precision in vocabulary selection, preservation of context, and overall comprehensibility. This study significantly enhances the current literature in machine translation and computational linguistics, especially for low-resource languages, by illustrating the practical effectiveness of a pivot-based method for augmenting translation precision. The method's dependability and efficacy in producing genuine translations were proved through numerous studies. The pivot-based technique enhances translation quality, although it possesses limitations, including the risk of error propagation and bias originating from the pivot language. Further research is necessary to examine the integration of named entity recognition (NER) to improve accuracy and optimize the intermediate translation process. This project advances the domains of machine translation and the preservation of low-resource languages, with practical implications for multilingual communities, language education resources, and cultural conservation.

Sanskrit is one of the earliest native languages and is correctly described as "the gods' language" because of its wide use in Indian religious literature from the past. However, it is becoming less popular in modern India. Due in significant part to the need for more materials for translation both in and out of Sanskrit, it is no longer commonly utilized. This study explores the feasibility of using machine translation (MT) to provide a link between Sanskrit and, one of the earliest native languages, and its contemporary descendant Hindi. A study was conducted between existing modelling methodologies, notably Statistical machine translation (SMT), and the proposed novel deep learning-based Machine translation strategy using a manually created parallel corpus for the Sanskrit-Hindi language pair. While SMT creates interpretations by mapping phrases from the languages of the source and destination, statistical models, and bilingual text corpora for learning parameters, neural machine translation (NMT) frequently models entire phrases in a single integrated model, using a convolutional neural network to calculate the probability of a word sequence. The proposed NMT model is implemented using an encoder-decoder with an attention mechanism paradigm and the inclusion of gated recurrent units. Our approach involved development of a novel model for Sanskrit-Hindi machine translation using deep learning and the creation of parallel corpora for the Sanskrit-Hindi language pair. The proposed model is evaluated on automated and human-based metrics, and results show that our proposed deep learning-based model outperforms statistical modelling techniques on Moses, surpassing them both with a BLEU score of 53.8% compared to 34.56%. This article examines the undiscovered area of machine translation from Sanskrit to Hindi and discusses the main benefits and drawbacks of statistical and neural machine translation while providing a fresh viewpoint on the subject.

Machine Translation (MT) systems are now being improved with the use of an ongoing methodology known as Neural Machine Translation (NMT). Natural language processing (NLP) researchers have shown that NMT systems are unable to deal with out-of-vocabulary (OOV) words and multi-word expressions (MWEs) in the text. OOV terms are those that are not currently included in the vocabulary that is used by the NMT system. MWEs are phrases that consist of a minimum of two terms but are treated as a single unit. MWEs have great importance in NLP, linguistic theory, and MT systems. In this article, OOV words and MWEs are handled for the Punjabi to English NMT system. A parallel corpus for Punjabi to English containing MWEs was developed and used to train the different models of NMT. Punjabi is a low-resource language as it lacks the availability of a large parallel corpus for building various NLP tools, and this is an attempt to improve the accuracy of Punjabi in the English NMT system by using named entities and MWEs in the corpus. The developed NMT models were assessed using human evaluation through adequacy, fluency and overall rating as well as automated assessment tools such as the bilingual evaluation study (BLEU) and translation error rate (TER) score. Results show that using word embedding (WE) and MWEs corpus increased the accuracy of translation for the Punjabi to English language pair. The best BLEU score obtained was 15.45 for the small test set, 43.32 for the medium test set, and 34.5 for the large test set, respectively. The best TER rate score obtained was 57.34% for the small test set, 37.29% for the medium test set, and 53.79% for the large test set, repectively.

Neural Machine Translation (NMT) performs sophisticated technology of translation between particular languages accurately and perfectly compared with traditional Statistical Machine Translation (SMT). Current models like Transformer and NMT can produce better translation results. In order to create NMT, we need a large-scale parallel corpus for training and validation of NMT data. Existing parallel corpus such as WMT English-German or WMT English-France become benchmark dataset or tasks in WMT. Conversely, ethnic language parallel corpus like English-to-Sundanese ethnic language is rarely used. This paper presents the collection and analysis of large-scale English-to-Sundanese parallel corpus. Our Parallel corpus encompasses various topics like sports, economics, politics and others. We produced a BLEU score English-to-Sundanese 38.91 that indicated our corpus valid.

Intuitively, machine translation (MT) between closely related languages, such as Swedish and Danish, is easier than MT between more distant pairs, such as Finnish and Danish.Yet, the notions of 'closely related' languages and 'easier' translation have so far remained underspecified.Moreover, in the context of neural MT, this assumption was almost exclusively evaluated in scenarios where English was either the source or target language, leaving a broader cross-lingual view unexplored.In this work, we present a controlled study of language similarity and neural MT difficulty for 56 European translation directions.We test a range of language similarity metrics, some of which are reasonable predictors of MT difficulty.On a text-level, we reassess previously introduced indicators of MT difficulty, and find that they are not well-suited to our domain, or neural MT more generally.Ultimately, we hope that this work inspires further cross-lingual investigations of neural MT difficulty.

Neural Machine Translation (NMT) has recently achieved the state-of-the-art in many machine translation tasks, but one of the challenges that NMT faces is the lack of parallel corpora, especially for low-resource language pairs. And the result is that the performance of NMT is much less effective for low-resource languages. To address this specific problem, in this paper, we describe a novel NMT model that is based on encoder-decoder architecture and relies on character-level inputs. Our proposed model employs Convolutional Neural Networks (CNN) and highway networks over character inputs, whose outputs are given to an encoder-decoder neural machine translation network. Besides, we also present two other approaches to improve the performance of the low-resource NMT system much further. First, we use language modeling implemented by denoising autoencoding to pre-train and initialize the full model. Second, we share the weights of the front few layers of two encoders between two languages to strengthen the encoding ability of the model. We demonstrate our model on two low-resource language pairs. On the IWSLT2015 English-Vietnamese translation task, our proposed model obtains improvements up to 2.5 BLEU points compared to the baseline. We also outperform the baseline approach more than 3 BLEU points on the CWMT2018 Chinese-Mongolian translation task.

One of the important applications for which natural language processing (NLP) is used is the machine translation (MT) system, which automatically converts one natural language to another. It has witnessed various paradigm shifts since its inception. Statistical machine translation (SMT) has dominated MT research for decades. In the recent past, researchers have focused on developing MT systems based on artificial neural networks (ANN). In this paper, first, some important deep learning models that are mostly exploited in Neural Machine Translation (NMT) design are discussed. A systematic comparison was done between the performances of SMT and NMT concerning the English-to-Bangla and English-to-Hindi translation tasks. Most of the Indian scripts are morphologically rich, and the availability of a sufficient corpus is rare. We have presented and analyzed our work and a survey was conducted on other low-resource languages, and finally some useful conclusions have been drawn.