Translation Techies @ D ravidian L ang T ech- ACL 2022-Machine Translation in D ravidian Languages (2022)

Abstract: This paper discusses the details of submission made by team Translation Techies to the Shared Task on Machine Translation in Dravidian languages- ACL 2022. In connection to the task, five language pairs were provided to test the accuracy of submitted model. A baseline transformer model with Neural Machine Translation(NMT) technique is used which has been taken directly from the OpenNMT framework. On this baseline model, tokenization is applied using the IndicNLP library. Finally, the evaluation is performed using the BLEU scoring mechanism.

## introduction a multilingual country such as india has a diversified population. several languages are spoken at various parts of the country @xcite . human spoken languages of india are divided into various groups. indo-aryan and dravidian languages are the two primary families. for almost 2600 years, there has been a recorded tamil literature @xcite @xcite @xcite . the earliest period of tamil literature, known as sangam literature, is said to have lasted from from 600 bc to ad 300. among dravidian languages, it possesses the oldest existing literature. the earliest epigraphic documents discovered on rock edicts and 'hero stones' date from the sixth century bc. in tamil nadu, the archaeological survey of india discovered over 60,000 of the 100,000 odd inscriptions discovered in india @xcite @xcite . however, the english language dominates the content available on the internet @xcite . it is a difficult task to have a human translator who can translate texts in across all language pairs. this forms the basic purpose of this shared task. we need constructive and precise computer algorithms that need minimal human intervention to bridge this massive language divide. machine translation can be used to complete this task effectively. with various conversational ais and voice assistants taking the world by storm, translation of native and low-resource languages has become imperative. the dravidian languages are morphologically rich in nature and are hence, difficult to deal with @xcite . the scripts are different when compared to the western scripts and require more attention @xcite @xcite . this task is an attempt to utilise the existing tools for translation of lowresource dravidian languages. the goal here is to develop a smooth algorithm which will help in knowledge dissemination, and end-to-end speech translation. we have used neural machine translation in our approach towards this problem. the rest of the paper is structured as follows: section 2 literature survey, section 3 methodology, section 4 results obtained from the shared task and section 5 conclusion and future scope. ## literature survey as a result of improved processing capabilities and training data, intensive research on mt began in the early 1950s @xcite , and it has progressed significantly since the 1990s. to accomplish more and more accurate machine translation, a variety of methodologies have been proposed @xcite . statistical machine translation(smt), a subtype of corpus-based translation, was the most extensively applied of them because it produced better results previous to the nmt systems. the statistics-based method to machine translation does not employ traditional language data. it functions on the basis of the probability principle. in this situation, the word in the source language corresponds to the comparable word in the target language. however, a large corpus of trustworthy translations in both the source and target languages is required. this strategy is comparable to that of ibm's research group in the early 1990s, which had some success with speech recognition and machine translation. nmt approaches are data driven and demands language resources such as a parallel corpora for translation. when it comes to large-scale translation projects like english to german and english to french, @xcite , it outperformed typical mt models. in recent years, several architectures for neural network-based machine translation have been proposed, including a simple encoderdecoder based model, an rnn based model, and an lstm model that learns problems with longrange temporal dependencies, as well as an attention mechanism-based model, which is machine translation's most powerful neural model. the evolution of machine translation approaches on indian languages was surveyed in detail giving an overview from rule based methods used, statistical machine translation methods implemented on the major indian languages(j., 2013). a sequence-to-sequence model based machine translation system for the hindi language was proposed @xcite which encouraged the use of nmt architecture on indian languages. the neural based approaches in machine translation have gained more scope as the accuracy improves based on the quality of the parallel corpora and it may be beneficial to develop an extension of the encoder-decoder paradigm that learns to align and translate together @xcite . transformer computes input and output representations using self-attention rather than sequence aligned rnns or convolution @xcite . this shared task addresses this issue and we have implemented the transformer model using open-nmt platform @xcite . the essential principles of n-gram precision are used by bleu @xcite to calculate similarity between the reference and created phrases. since it employs the average score of all discoveries in the test dataset rather than presenting results for each sentence. hence, we have used the bleu metric for the model in this paper. the base model chosen is transformer architecture on opennmt framework and we have further enhanced this model and applied to given five lan-guage pairs. the results are tabulated based on bleu metric. ## methodology this task explores the transformer approach in opennmt framework. with less resources in hand, the opennmt framework offers best models to experiment upon. the baseline model was a transformer architecture directly borrowed from the opennmt framework and used on the dravidian language pairs. [opennmt-py toolkit with commands] the model was used for five language pairs with different sizes of training, validation and testing data as shown in ## results the sample text for five language pairs based on training data is shown in figure 1 . after running the model on all the five language pairs of kannada-malayalam, kannada-sanskrit, kannada-tamil, kannada-telugu , and kannada-tulu; the following bleu scores in ## conclusion and future scope this paper describes the details of submission made by team translation techies to the shared task on machine translation in dravidian languages-acl 2022. the transformer architecture present in opennmt framework along with modifications is implemented in this shared task. the current model can be further improved by providing larger datasets and pre-processing them in detail. we can use data augmentation and byte-pair encoding techniques as well. subword tokenization is also a good technique to alleviate the problem with such low-resource language pairs @xcite . the efficient translation of the dravidian languages is necessary as the need for smart systems are rising rapidly.