An Attention-Based Neural Translation System for E nglish to B odo (2025)

Abstract: Bodo is a resource scarce, the indigenous language belongs to the Tibeto-Burman family. It is mainly spoken in the north-east region of India. It has both linguistic and cultural significance in the region. Only a limited number of resources and tools are available in this language. This paper presents a study of neural machine translation for the English-Bodo language pair. The system is developed on a relatively small parallel corpus provided by the Low-Resource Indic Language Translation as a part of WMT-2025. The system is evaluated by the WMT-2025 organizers with the evaluation matrices like BLUE, METEOR, ROUGE-L, chrF and TER. The result is not promising but it will help for the further improvement. The result is not encouraging, but it provides a foundation for further improvement.

## introduction the bodo language, which belongs to the sino-tibetan language family, is one of the widely spoken languages in assam and several other parts of the north-eastern states of india. it is used predominantly in the bodoland territorial region (btr), which includes the districts of kokrajhar, chirang, baksa, and udalguri, as well as in other districts such as kamrup, sonitpur, lakhimpur, nagaon, morigaon, and karbi anglong. bodo is one of the 22 languages listed in the eighth schedule of the indian constitution and is officially recognized by the government of india @xcite . according to the 2011 census, it is spoken by more than a million people, primarily members of the bodo community @xcite . the number of bodo speakers is shown in the figure 1 . the bodo language has rich linguistic features and uses the devanagari script for writing, similar to 1 https://www2.statmt.org/wmt25/indic-mt-task.html hindi. having the tonal feature. hence, effective techniques are not developed to capture all these features @xcite . machine translation is a core application in the field of natural language processing (nlp). with advancements in computational power, the focus has shifted from rule-based methods to machine learning and deep learning approaches. however, implementing deep learning techniques requires a large volume of data @xcite . in this paper, we have developed an english-bodo machine translation system using a transformerbased neural machine translation approach. preprocessing steps such as tokenization, subword extraction, and normalization are required before feeding the data into the actual transformer model @xcite . ## related work as mentioned above, bodo is a low-resource language @xcite , and development a machine translation system for it faced significant challenges due to the limited availability of parallel corpora and digital resources @xcite . most research efforts have focused on creating and expanding parallel corpora, as well as adapting machine translation techniques to function effectively with scarce data. although work in bodo machine translation remains limited, there have been some notable efforts, particularly in building english-bodo translation systems. a brief overview of these works is provided below. @xcite observed that traditional neural machine translation and statistical machine translation models have an alignment problem that affects the performance. the common encoder-decoder architecture, which uses an encoder to compress a source sentence into a single, fixed-length vector, faced a critical bottleneck: this fixed-length vector was often insufficient to capture all the information from a long sentence. to solve this, they proposed a new model that allows the decoder to automatically search for and focus on the most relevant parts of the source sentence when predicting each word of the translation. this mechanism, known as attention, significantly improved translation quality by overcoming the limitations of a single context vector. verma et al. @xcite conducted a literature survey on neural machine translation (nmt), highlighting the advantages of the nmt architecture. vaswani et al. @xcite proposed the attention-based transformer model, which gained significant popularity in machine translation due to its novel architecture and promising performance. parvez et al. @xcite attempted neural machine translation for the pair of low-resource languages of english and bodo. they utilized a relatively small english-bodo parallel corpus and implemented their system using the opennmt-py framework. their model achieved a highest bleu score of 11.01. @xcite worked on bodo-to-english machine translation using a phrase-based statistical machine translation (pb-smt) approach. they applied this technique to bodo-english parallel corpora in both the general and news domains, reporting a highest bleu score of 30.13. narzary et al. @xcite developed an attention-based english-bodo neural machine translation system using data from the tourism domain. their baseline model achieved a bleu score of 11.8. by incorporating an attention mechanism, they improved the model's performance, reaching a bleu score of 16.71. talukdar et al. @xcite focused on assamese-bodo neural machine translation and investigated the impact of data quality and quantity on trans-lation performance. they iteratively augmented the dataset and evaluated the outcomes at each stage. the experiments were conducted using the opennmt-py framework. @xcite suggested that the use of a highresource language as a pivot can improve translation into related low-resource languages. they conducted experiments on machine translation of the english to indian language -specifically translating english into konkani, manipuri, sanskrit, and bodo -employing hindi, marathi, and bengali as pivot languages. ## system description we have implemented the attention-based neural machine translation (nmt) system. it is a deep learning model specifically designed for sequenceto-sequence tasks, such as translating text from one language to another. in traditional sequenceto-sequence models, the entire input sentence is encoded into a single, fixed-length vector that captures its relevant contexts. that single vector cannot effectively capture all the rich context of long or complex sentences. on the other hand, attentionbased architecture allows the model dynamically to focus on relevant parts of the input sentence while generating each word in the output. the system typically consists of an encoder-decoder architecture along with an attention mechanism. the attention mechanism dynamically modifies the context vector for each output word. this allows the decoder to "attend" to different parts of the input sentence at each step. the basic encoder-decoder architecture, along with the attention mechanism, is depicted in figure 2 . the figure is configured for the english-bodo translation, which is influenced by the tato et al. @xcite diagram. the attention mechanism used in the decoder to decide which parts of the input sequence to focus on while generating an output. calculating a context vector by taking a weighted sum of the encoder's hidden states. the weights for this sum are dynamically adjusted, giving more importance to the input words that are most relevant to the current output being generated. based on this mechanism, the model can capture long-range dependencies and produce higher-quality translations. this is particularly effective for long or complex sentences or when translating between languages with different word orders. in the attentional model (dzmitry bahdanau, 2015), all hidden states h i of the encoder are utilized to compute the context vector c t . this model generates a variable-length alignment vector a t , whose size corresponds to the number of time steps on the source side. the alignment vector is obtained by comparing the current hidden state h i with each encoder hidden state hs . where: here, both the multiplicative and additive (luong et al., 2015) (dzmitry bahdanau, 2015) attention mechanisms have normalized variants, known respectively as scaled luong and normed bahdanau @xcite . the main idea behind the attention vector is to determine how much emphasis should be focused on each source word at a given time step. a higher value in the attention weight at indicates that the corresponding source word has a greater influence on predicting the next word in the output sentence. particularly, the model the model performs translation based on the conditional probability p(y|x), which represents the likelihood of translating a source sentence x 1 ; ....; x n , into a target sentence y 1 ; ....; y m . this is achieved using an encoder-decoder framework. ## dataset used the data set we used in this work for the translation task is provided in the very limited training data setting of the wmt25 indic multilingual machine translation shared task foot_0 , a snapshot of the data is shown in figure 3 and figure 4 respectively. bodo is a low-resource language, and the availability of high-quality parallel corpora is significantly constrained. the official data set provided by the wmt25 organizers consists of a small number of english-to-bodo sentence pairs curated for the purpose of benchmarking machine translation systems under low resource conditions. in the dataset, contains training, development, and test splits, with the training set including only a few thousand sentence pairs. these sentences span general purpose domains such as basic conversational language. the development set was used for validation and tuning, while the test set was reserved for blind evaluation by the task organizers. we have also taken a parallel data set focused on tourism foot_1 augmented with wmt25 data for training purposes shown in all model training and experimentation were conducted using google colab, a cloud-based development platform. we utilized the nvidia a100 gpu available through google colab for training our nmt model. the a100, based on nvidia's ampere architecture, offers high memory bandwidth and massive parallel processing capabilities, making it a well suited for deep learning tasks. its support for mixed precision training and large batch processing significantly accelerated model training and testing. the gpu enabled efficient handling of our encoder to decoder architecture with attention which allowed us to train and test on the english to bodo dataset with reduced computation time and improved performance. the model is developed and trained using py-torch within a google colab environment. the data set is cleaned by removing null values and then shuffled to eliminate order bias. we load a test set provided as plain text files, ensuring that sentence alignment is preserved across the splits of training and validation sets. for preprocessing, we utilize a custom languageprocessor class that tokenizes the data and constructs a vocabulary with special @xmath0, <sos>, <eos>, @xmath1. it maps words to indices and vice versa and computes the maximum sentence length for padding. we configured a sequence-to-sequence encoderdecoder model with attention using carefully selected hyperparameters to ensure efficient training and optimal performance. the embedding dimension for both the encoder and decoder was set to 256, while the hidden state dimensions were set to 512 units. the dropout regularization was applied with a rate of 0.5 in both the encoder and decoder. training was conducted using a batch size of 64 over 15 epochs. ## result the system is evaluated by the wmt25 organiser, which provided a test dataset of 1,000 sentences. a total of nine runs were assessed for the task. five evaluation metrics were used: bleu, meteor, rouge-l, chrf, and ter foot_2 . the result of our system is shown on ## conclusion it is observed that your result (