Context-Aware Neural Translation Framework: Enhancing

Multilingual Accuracy and Real-World Adaptability through

Optimized Deep NLP Models

Jaisharma K

, Baiju Krishnan

, Sumathi.B

, P. Chellammal

, Vaithiyanathan R

and A Nagamani

Departmetn of CSE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai,

Tamil Nadu, India

Department of English and Other Indian & Foreign Languages, Vignan's Foundation for Science, Technology & Research

(Deemed to be University), Deshmuki, Hyderabad Campus, Telangana State, India

Department of Information Technology, Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College, Chennai,

Tamil Nadu, India

Department of Computer Science and Engineering, J.J.College of Engineering and Technology, Tiruchirappalli, Tamil

Nadu, India

Department of CSE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

Department of Computer Science and Engineering MLR Institute of Technology, Hyderabad-500043, Telangana, India

Keywords: Neural machine translation, multilingual NLP, transformer models, context-aware translation, semantic

alignment.

Abstract: Given the pressing need for accurate and real-time multi-lingual communication, in this paper, we introduce

a context-aware neural translation framework aimed at raising the quality of MT across varying languages

and domains. Through the introduction of transformer-based architectures, domain-adaptive fine-tuning, and

semantic alignment mechanisms, the model mitigates issues with low-resource language performance and

semantic distortion, as well as zero-shot translation inconsistency. It incorporates hybrid evaluation

techniques and works with real-world data-sets to develop its robustness, adaptability and linguistic fidelity.

Furthermore, model optimization strategies are implemented to trade-off between computational efficiency

and output quality. The proposed approach not only solves crosslanguage gap problem across the world, but

also outperforms state-of-the-art NMT system.

1 INTRODUCTION

In the interconnected age of the internet, it’s more

important than ever that we have a way talk with

each other across languages. In a world where

international connections cut across education,

business, diplomacy and healthcare, machine

translation (MT) plays a leading role in overcoming

language barriers. Classical statistical and rule-based

translation systems are being increasingly replaced

with more flexible neural methods, fueled by the

advances in deep learning and natural language

processing (NLP). Yet, despite the progress with

transformer-based architectures and multi-lingual

models, several challenges still persist – especially in

terms of producing context-aware translations,

addressing low-resource languages, and preserving

semantics across the complexity of sentence syntax.

Neural Machine Translation (NMT) transfigures

the automatic language translation scene, through

attending mechanisms, parallel processing and

encoder-decoder networks. However, these advances

tend to fail when it comes to practical usage given

the domain-specific, cultural-insensitive, and low-

resource requirements. Generalized models fail

when encountering out-of-distribution texts or

idiomatic texts that need more than direct word

substitutions. Furthermore, it becomes more

problematic as Cross Linguistic Data have

emphasized; as the use of English-centric training

K, J., Krishnan, B., B, S., Chellammal, P., R, V. and Nagamani, A.

Context-Aware Neural Translation Framework: Enhancing Multilingual Accuracy and Real-World Adaptability through Optimized Deep NLP Models.

DOI: 10.5220/0013859100004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 1, pages

137-144

ISBN: 978-989-758-777-1

137

corpora is growing, many regional and minor

languages are becoming underdeveloped.

In this paper, we aim to fill the gap by presenting

a reliable, contextually enhanced neural machine

translation framework, which integrates the

advantages of semantic embedding, transformer

optimization, and adaptive fine-tuning. The goal is to

achieve higher level of translation quality across a

variety of languages, including low-resource ones,

with real-time applications and linguistic richness. By

this means, this work not only improves MT systems

but also facilitates global communication in an

inclusive manner.

2 PROBLEM STATEMENT

Despite recent progress in neural machine translation,

the performance of current approaches remains

unsatisfactory over a variety of language pairs and

domains. Existing model design still work well in

high-resource language set- tings and where the

translation of examples can be independent of the

context in which they are used, but seem to have

insufficient capacity for low- resource language

settings, context dependent translations and other

cases where idiomatic or do- main specific

translations are required. Current best models are

trained on high-resource, English-focused corpora,

leading to biased outputs and poor generalization for

under-served languages. Furthermore, the lack of

contextual awareness often brings semantic drift,

especially for longer document or multi-turn

conversation. While transformer architectures have

increased the state of the art, such methods are still far

from guaranteeing retained linguistic nuance and

cultural impact when used in practice. While

transformative, these constraints limit the potential

for machine translation in global communication, and

require a more flexible, context-dependent and

inclusive model of neural language translation.

3 LITERATURE SURVEY

The trajectory of machine translation (MT) has been

influenced by incremental advances in processing

natural language (NLP), especially in the era of neural

models. The early NMT architectures such as the

encoder-decoder framework with attention

(Bahdanau et al., 2015) paved way for translation

systems to be learn contextual depen- dencies.

Approach the Transformer introduced by Vaswani et

al. (2017) significantly advanced the state of the art

by allowing parallel processing and attention-based

context learning, which led to major speed and quality

gains in translation.

Multilingual neural models have subsequently been

developed to cover many languages. Johnson et al.

(2017) but showed its work with zero-shot

translation where the model could translate between

language pairs it had not been explicitly trained on.

But this model and others, for example, Aharoni et

al. (2019) and Arivazhagan et al. (2019), suffer from

semantic drift and poor in low-resource settings have

been questioned. Liu et al. (2020) mitigated this issue

via multilingual denoising pre-training, but it works

less well in the absence of domain-sensitive fine-

tuning. Table 1 shows the Dataset Composition by

language pair.

In this recently, situation-based enhancement and

evaluation have been tried to overcome these

problems. Freitag et al. (2021) investigated meta-

evaluation methods that yield a more accurate

estimation of translation quality but stressed that

human-based evaluations had flaws. Zhang and Zong

(2021) proposed deep attention are also reported to

perform better at the sentence level, but they do not

have discourse level understanding. Additionally,

Tan et al. (2019) used knowledge distillation for

multilingual models, but is very dependent on the

quality of teacher model.

Despite the availability of open-source toolkits such

as NiuTrans (Xiao et al., 2021) and OPUS-MT

(Tiedemann and Thottingal, 2020) that facilitate the

quick development of translation systems, such

pipeline-style systems tend to exhibit inconsistent

quality across user domains and under-resourced

languages. Fan et al. (2021) paved the way for

inclusive multilinguality by introducing architectures

which go beyond English-biasedness, albeit with

prohibitive computational overhead. Ashraf (2024)

and Tran et al. (2025) have also more recently

highlighted the issues of domain adaption and

semantic preservation, which emphasise the necessity

of robust, real-time and context-aware models.

In conclusion, despite the advances made by the

latest NMT models, existing models continue to

experience challenges in context preservation,

linguistic structure diversity, and low-resource

scenarios. These issues are the basis on which we

proposed a new context-aware neural network aimed

to remedy these long-standing challenges.

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4 METHODOLOGY

The proposed approach presents a hybrid neural

machine translation (NMT) model and prioritizes

context-awareness, semantic preservation and cross-

lingual generality over diverse language pairs,

especially in low-resource scenarios. The system

consists of five main parts: data pre-processing,

model architecture, training procedure, context

incorporation and evaluation protocol.

The basis of the translation setup is a broad

multilingual dataset of parallel corpora from

Europarl, JW300, and OPUS. For simulating realistic

transfer scenarios there are high-resource (e.g.

English-French) and low-resource (e.g. Tamil-

English) language pairs in the data set. Preprocessing

includes language-specific tokenization, strip non-

standard characters & normalization. For rare and

morphologically rich words, subword segmentation

with Byte Pair Encoding (BPE) technique is adopted.

Table 1: Dataset Composition by Language Pair.

Langua

ge Pair

Type Resour

Level

No. of

Sentence

Pairs

Source

Dataset

English

–French

High-

resource

Formal 1,200,00

Europar

English

–

German

High-

resource

Mixed 950,000 WMT2

English

–

Sinhala

Low-

resource

Formal 110,000 Flores-

101

English

–Tamil

Low-

resource

Informa

85,000 OPUS

English

–

Amhari

Low-

resource

Mixed 95,000 JW300

4.1 Transformer-Based Architecture

with Contextual Modules

The model architecture is based on the vanilla

Transformer (Vaswani et al., 2017) with alterations to

ensure semantic consistency and discourse-level

translation. The encoder-decoder layers are

augmented with dual attention; namely, intra-

sentence attention, for local coherence, and inter-

sentence attention, for contextual flow across

paragraphs. Extra positional encoding layers are

added to process not only the previous and next

sentence but also to model sentence transitions.

4.2 Adaptive Fine-Tuning and

Transfer Learning

In order to reduce the gap of low-resource language

translation, the model is first pre-trained on

multilingual corpora with denoising autoencoding

and then fine-tuned on the language pair. We build on

a teacher- student knowledge distillation framework

to provide guidance for training a low-resource model

with a high-resource one serving as the teacher.

Adapter modules are interposed between the layers

of the transformer to facilitate cost-effective domain-

specific fine-tuning without retraining the complete

model. Table 2 shows the Model configuration

Parameters.

Table 2: Model Configuration Parameters

Paramete

Value

Number of Layers

(Encoder/Decoder)

6 / 6

Hidden Size 512

Number of Attention Heads 8

Tokenizer

Byte Pair

Encoding

Pretrained Embeddin

Use

XLM-R Base

Optimize

AdamW

Learnin

Rate 3e-4

4.3 Context-Aware Embedding and

Semantic Alignment

To facilitate the preservation of meaning, during

training, the framework integrates a semantic

alignment module. Sentence embeddings are based

on a pre-trained multilingual language model (e.g.

XLM-R or mBERT) and form the basis for the

contextual signals. The embed-dings get aligned

using a cosine-similarity loss to assure that translated

sen-tences preserve the semantics. And it tracks the

mapping between sentences and references during the

translation process with a context encoder, which

Context-Aware Neural Translation Framework: Enhancing Multilingual Accuracy and Real-World Adaptability through Optimized Deep

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works well with documents, dialogue, and long-form

content.

4.4 Evaluation with Hybrid Metrics

and Real-World Scenarios

both traditional (BLEU, METEOR, TER) and

semantic (BERTScore) Translation quality is

measured with traditional metrics as well as semantic

scores such as BERTScore. We propose a custom

contextual coherence metric for quantifying sentence

transition quality in paragraphs. Human evaluators

also evaluate fluency, adequacy, and culture

appropriateness in diverse and domain-specific text

genres such as healthcare, legal and conversation.

Online testing allows us to simulate user queries in

different languages and dialects on a web interface to

evaluate the model performance.

Figure 1: System Architecture of the Context-Aware Neural

Translation Framework.

Our approach, therefore, guarantees that the

translation system will not only be correct at a

sentence level but also will support longer discourse,

adapt to domain-specific idiosyncrasies, and work in

a wide range of linguistic environments, while

leading to lower computational complexity. The

complete pipeline of the neural machine translation

system we propose is shown in Figure 1.

5 RESULT AND DISCUSSION

The context-aware neural translation framework is

evaluated based on quantitative metrics and

qualitative comparison on multiple datasets and

language pairs. The latter describes properties of the

performance, a domain specific test, comparison to

baseline models and highlights from real world

usability.

5.1 Quantitative Evaluation

First, the system was evaluated with well-known

translation metrics BLEU, METEOR and

BERTScore. Results WMT’21 tasks (English↔

German and English↔ French) Our model obtained

43.7 BLEU for English→ German and 47.2 BLEU

for English→ French on the WMT’21 dataset, which

outperforms the BLEU of the Transformer baseline

(39.3 and 43.5 respectively). Approximations of +3.5

improvements were observed for the METEOR

scores too, reflecting improvements of adequacy and

fluency.

Figure 2: BLEU Score Comparison for Different

Translation Models.

For low-resource language pairs such as

English↔Sinhala and English↔Amharic (from the

Flores-101 dataset), the model scored +6 BLEU

points higher than Google’s multilingual model

baseline. This gain can be attributed to the adaptive

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fine-tuning and cross-lingual transfer learning

strategies, which leveraged knowledge from high-

resource languages to boost performance in

underrepresented ones. “BLEU score improvements

over baseline systems are illustrated in Figure 2.”

Table 3: Evaluation Results on Benchmark Datasets

Model

Language

Pai

BLE

METE

BERTS

core

Baseline

Transforme

English–

German

39.3 35.1 0.882

Proposed

Framework

English–

German

43.7 38.6 0.928

Baseline

Transforme

English–

Sinhala

14.8 11.2 0.801

Proposed

Framework

English–

Sinhala

21.0 15.3 0.882

BERTScore further revealed the model’s

semantic accuracy, with scores consistently above

0.92, indicating high alignment of meaning between

source and translated sentences. This highlights the

impact of semantic alignment layers and context-

aware embeddings integrated within the architecture.

5.2 Discourse-Level and Contextual

Coherence

In addition to sentence-level metrics, our system was

tested for document-level coherence using a custom

contextual coherence metric (CCM). CCM evaluates

how well sentence transitions are maintained when

translating multi-sentence paragraphs. Compared to

the baseline Transformer, our model improved

discourse coherence by 18%, especially in legal and

technical documents where flow and consistency are

crucial. “Figure 4 highlights the proposed model’s

superiority in maintaining discourse-level

coherence.”

Figure 3: Contextual Coherence Scores Across Models.

Manual inspection confirmed that the context-aware

module effectively resolved co-reference ambiguities

and maintained subject continuity across longer texts.

For example, in conversational data from TED talk

transcripts, the model retained the speaker's tone and

inferred implicit subject-object references more

accurately than existing models.

5.3 Domain-Specific Evaluation

The model was tested across three specific domains

medical, legal, and general conversational texts. Each

domain posed unique challenges:

● Medical domain: Required accurate

terminology translation and sensitivity to

context. The proposed model achieved

95.4% term accuracy, correctly translating

complex medical terms that baseline models

often omitted or misinterpreted.

● Legal domain: Context-aware translation

proved essential, especially with clauses and

regulatory language. Here, the system

maintained sentence integrity and clause

separation, reducing legal ambiguities. “As

shown in Figure 3, the proposed model

excels in fluency and term accuracy across

different application domains.”

Figure 4: Translation Accuracy and Fluency Scores by

Domain.

● Conversational domain: Focused on

fluency and cultural nuance. The system

demonstrated better idiomatic translation

and informal phrasing, scoring higher in

human fluency assessments compared to

GPT-based models.

5.4 Real-Time Application and

Performance

A real-time prototype was deployed as a web

interface, allowing user interaction in multiple

languages. The system was able to generate

translations with an average latency of 320ms,

Context-Aware Neural Translation Framework: Enhancing Multilingual Accuracy and Real-World Adaptability through Optimized Deep

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demonstrating efficiency even with the added

contextual modules. When benchmarked on devices

with limited computational power, the model's

adapter-based architecture allowed for reduced

memory consumption while preserving translation

quality. “Performance per language pair, including

low-resource ones like Sinhala and Tamil, is shown

in Figure 5.”

Figure 5: BLEU Score by Language Pair for Proposed

Model.

Usability testing with bilingual users revealed

84% satisfaction with the translations’ naturalness

and accuracy. Participants noted that the translated

content felt more "human-like" and contextually

consistent, particularly in narrative and

conversational settings.

5.5 Comparative Analysis

The model was compared against several strong

baselines, including:

● Google Translate API

● OpenNMT Transformer

Facebook’s M2M-100 multilingual model

Across all test sets, the proposed framework

outperformed these models in contextual handling,

semantic accuracy, and domain adaptability. Notably,

while Google's API performed well for general-

purpose translations, it struggled with specialized

vocabulary and low-resource languages. M2M-100

offered strong multilingual support but was less

effective in discourse coherence and context

retention. Table 4 shows the analysis with existing

systems.

Table 4: Comparative Analysis with Existing Systems.

System

Avg

Contex

tual

Handli

Real-

Time

Readine

Low-

Resourc

Support

Google

Translat

37.5

Mediu

High Low

Faceboo

k M2M-

100

39.2

Mediu

Medium Medium

Propose

Framew

44.5 High High High

5.6 Limitations and Considerations

While the model achieves high accuracy, certain

challenges remain. For morphologically complex

languages like Hungarian and Finnish, translations

occasionally suffered from inflectional

inconsistencies. Also, in highly creative or poetic

texts, the model leaned toward literal translation

rather than interpretive phrasing. Future iterations

could integrate stylistic transfer modules to address

these edge cases. Figure 6 visualizes the distribution

of evaluation metrics, providing a holistic

performance summary.”

Figure 6: Evaluation Metric Distribution for Proposed

Framework.

Additionally, while adapter-based tuning is

efficient, its performance slightly lags in extremely

domain-specific datasets without adequate

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pretraining examples. Expanding the dataset with

more niche domains could help in further refining

performance.

6 DISCUSSION

The results clearly indicate that introducing context-

aware mechanisms and semantic alignment into

neural translation significantly improves both

translation accuracy and fluency. By addressing

discourse-level coherence, low-resource adaptability,

and semantic preservation, the proposed model not

only enhances current NMT capabilities but also sets

a foundation for broader, more inclusive global

communication. The integration of real-time

inference capabilities further positions the system for

practical deployment in diverse linguistic settings.

7 CONCLUSIONS

The growing need for accurate and contextually

meaningful translation in our globally connected

world demands more than just literal language

conversion it calls for systems that understand

nuance, cultural context, and linguistic diversity. This

research has proposed a novel context-aware neural

translation framework that successfully addresses

several limitations of traditional and current neural

machine translation models. By combining

transformer-based architectures with semantic

alignment, domain-adaptive fine-tuning, and real-

time inference capability, the system delivers not only

high translation accuracy but also meaningful,

human-like communication across multiple

languages.

Experimental results have demonstrated the

model's strength in handling low-resource language

pairs, maintaining discourse-level coherence, and

adapting effectively to different domains such as

healthcare, legal documentation, and conversational

text. The hybrid evaluation strategy has shown that

the model not only performs well in standard metrics

like BLEU and BERTScore but also excels in

capturing contextual flow and semantic integrity key

indicators of natural translation.

In moving beyond English-centric paradigms and

emphasizing inclusivity in language processing, this

framework contributes to bridging digital and

communicative divides across communities

worldwide. It sets the groundwork for future

innovations in real-time multilingual systems,

educational tools, cross-border services, and more. As

machine translation continues to evolve, context

sensitivity, linguistic depth, and computational

efficiency will remain at the core of truly

transformative NLP applications and this work takes

a significant step in that direction.

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