MUCS @ - Machine Translation for D ravidian Languages using Stacked Long Short Term Memory (2021)

Abstract: Dravidian language family is one of the largest language families in the world. In spite of its uniqueness, Dravidian languages have gained very less attention due to scarcity of resources to conduct language technology tasks such as translation, Parts-of-Speech tagging, Word Sense Disambiguation etc,. In this paper, we, team MUCS, describe sequence-to-sequence stacked Long Short Term Memory (LSTM) based Neural Machine Translation (NMT) model submitted to “Machine Translation in Dravidian languages”, a shared task organized by EACL-2021. The NMT model was applied on translation using English-Tamil, EnglishTelugu, English-Malayalam and Tamil-Telugu corpora provided by the organizers. Standard evaluation metrics namely Bilingual Evaluation Understudy (BLEU) and human evaluations are used to evaluate the model. Our models exhibited good accuracy for all the language pairs and obtained 2nd rank for TamilTelugu language pair.

## introduction human being is a social entity and they love to communicate with each other and live together from ancient times. they communicate with each other through different means of communication and exchange their data/information/thoughts with each other. initially, sign language was the means of communication and it was used to exchange thoughts with each other, but now, in this era of technology, there are many ways to communicate out of which language plays a major role. there are more than thousands of languages used all over the world, even in india, due to different religion, culture and tradition. among indian languages dravidian is a language family consisting of four long literary languages, namely, tamil, kannada, telugu and malayalam along with tulu and kodava which are small literary languages. all these languages except kodava have their own script. further, these languages consists of 80 different dialects foot_0 namely brahui, kurukh, malto, kui, kuvi, etc. dravidian languages are mainly spoken in southern india, sri lanka, some parts of pakistan and nepal by over 222 million people @xcite . it is thought that dravidian languages are native to the indian subcontinent and were originally spread throughout india 1 . tamil have been distributed to burma, indonesia, malaysia, fiji, madagascar, mauritius, guyana, martinique and trinidad through trade and emigration. with over two million speakers, primarily in pakistan and two million speakers in afghanistan, brahui is the only dravidian language spoken entirely outside india (ethnologue) foot_1 . rest of the dravidian languages are extensively spoken inside india and by south indians settled throughout the world. these languages share similar linguistic features and few of them are listed below @xcite : though these languages have rich collection of resources, they are still considered as underresourced languages foot_2 due to the availability of very less digital resources and tools. most of the south indians speak/understand only their native language as a means of communication. further, because of migration people need to learn the local languages to survive. in this regard, language translation (lt) technology gains importance as it provides the easy means of communication between people when language barrier is a major issue. various lt technologies are available, namely, manual translation or human translation, machine aided translation and automatic translation or machine translation (mt). as mt is fast, inexpensive, reliable and consistent compared to other lt technologies, they are gaining popularity. ## related work many attempts are being carried out by the researchers to give special attention for dravidian language family and several researches have made noticeable work in this direction @xcite . nmt is a promising technique to establish sentence level translation and suitable preprocessing techniques will share their contribution for better performance. observing this @xcite have applied byte pair encoding (bpe) for their model to resolve out-of-vocabulary problem. they used entam v2.0 dataset that contains english-tamil parallel sentences. their model exhibited improvement in bleu score of 8.33 for bidirectional lstm+adam (optimizer) + bahdanau (attention) + bpe + word embedding. multi-modal multilingual mt system utilizing phonetic transcription was implemented by @xcite using under-resourced dravidian languages. as a part of this work they have released mmdravi -a dravidian language dataset that comprises of 30,000 sentences. they have conducted both statistical machine translation (smt) and nmt and the nmt model outperformed smt in terms of bilingual evaluation under study (bleu) score. @xcite have proposed a novel machine learning based translation approach for kannada-telugu dravidian language pair considering wikipedia dataset. further, they considered english-kannada and english-telugu language pairs for illustrating the efficacy of their model. they have proposed n-grams based connecting phrase extraction and these extracted phrases are trained in multilayered neural network. for testing, the phrases are extracted from test dataset and alignment score is computed and then these phrases are used for post-processing. they have observed a considerable accuracy of 91.63%, 90.65% and 88.63% for english-kannada, english-telugu and kannada-telugu, respectively. dictionary based mt for kannada to telugu is developed by @xcite and as a part of this work, a bilingual dictionary with 8000 words is developed. providing suffix mapping table at the tag level, this model uses dictionary for translating word by word without giving much attention on the correlation between words and has shown more than 50% accuracy. ## methodology though many attempts are being carried out to develop mt system for dravidian languages, development of full fledged mt system for dravidian languages is an open ended problem as these languages are agglutinative and morphologically rich (chakravarthi, 2020). in the proposed work, sequence-to-sequence stacked lstm is used to build the translation system using the dataset provided by the organizers foot_3 . ## conclusion and future work this paper describes the models submitted to "machine translation in dravidian languages" shared task to perform translation of english-tamil, english-telugu, english-malayalam and tamil-telugu language pairs. a close analysis of the corpora is carried out before performing the preprocessing and the corpora are found challenging to establish translation. though all the corpora belong to low-resource languages and being rich in morphology, this sequence-to sequence stacked lstm model exhibits the satisfactory results for all the corpora. all the models exhibited considerable performance and our model obtained 2 nd rank for tamil-telugu language pair. in future, we would like to explore different nmt techniques that helps to translate low-resource languages efficiently.