Date: 11-11-2024

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Machine translation using SMT and Hybrid model using natural language processing

Abstract:
Machine Translation is the translation of text by a computer with no human involvement. It is research with
different types methods being created, like rule-based, statistical and example- based machine translation.
Neural networks have made a leap forward to machine translation. This paper discusses the building of a
deep neural network that functions as a part of end-to- end translation pipeline. The completed pipeline
would accept English text as input and return the Hindi Translation. The project has three main parts which
are preprocessing, creation of models and Running the model on English.
Introduction
Machine Translation (MT) has seen significant advancements over the last few decades. Traditional SMT
systems and more recent NMT models each have their strengths and weaknesses. This paper proposes a
hybrid system that combines SMT and word embeddings, with the goal of improving the robustness of MT
systems. We show that integrating word embeddings trained via neural networks into an SMT framework
enhances translation quality, particularly for phrase-based translations

1.1 Problem Statement

SMT systems often suffer from fluency issues and lack the ability to effectively capture semantic context. On
the other hand, NMT systems, while strong in general, can struggle with domain-specific language or low-
resource settings. This research explores how a hybrid SMT-NMT system with word embeddings can mitigate
these issues.

2.Related Work
2.1Statistical Machine Translation (SMT)
SMT has been a most used approach in machine translation until the rise of neural methods. In SMT,
translation is treated as a probabilistic process, relying on large bilingual corpora to generate translation
rules.

2.2Neural Machine Translation (NMT)

NMT models, based on deep learning architectures like RNNs, LSTMs, and more recently transformers, have
made breakthroughs in translation accuracy. However, NMT systems are often data-hungry and can struggle
in domains where parallel corpora are limited. MarianMT is one of the state-of-the-art NMT models that
provides highly accurate translations for various language pairs.

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2.3Word Embeddings
Word embeddings like Word2Vec and GloVe represent words in a continuous vector space, capturing
semantic similarity between words. These embeddings have proven valuable in numerous NLP tasks and can
enhance traditional models by providing contextual word meanings.

3.Proposed Methodology
This paper proposes a three-step translation pipeline:

1.Word Embedding Training: Word2Vec is used to train word embeddings on a large corpus of English text. The
embeddings capture semantic similarities between words, which can be leveraged to refine the output of
SMT.
2.SMT Translation: An SMT model generates initial translations, treating the problem as a phrase-based
probabilistic task.
3.Embedding-Enhanced Refinement: Word embeddings are used to enhance the SMT output by suggesting
semantically related words.
4.Final Translation with NMT: The enhanced translation is passed to an NMT model (MarianMT) to produce the
final translation output.
Loss function’s equation in detailed:

3.1Word2Vec Training
We train a Word2Vec model on the Brown corpus using a skip-gram approach, with a vector size of 100 and a
context window of 5. This produces embeddings that can identify semantically related words.

3.2SMT Output Generation

A standard phrase-based SMT system is used as a baseline to produce initial translations. While SMT is
effective for phrase translation, it often lacks fluency and coherence when handling complex sentences.

3.3Embedding-Based Enhancement
The SMT output is refined by replacing words with their semantically closest alternatives, as determined by
the Word2Vec embeddings. This step improves the fluency and meaning of the translated sentence.

3.4NMT Post-Processing
Finally, the enhanced SMT output is passed through MarianMT, an NMT system, for further refinement. The
NMT system benefits from the higher quality input generated through the previous steps.

4.Experimental Setup
4.1Evaluation Metrics
To evaluate translation quality, we use BLEU (Bilingual Evaluation Understudy) and METEOR (Metric for
Evaluation of Translation with Explicit Ordering) scores, comparing translations from the hybrid system
against those from SMT and NMT models alone.

5.Results and Comparative Analysis

5.1Quantitative Results

5.1.1BLEU Scores
BLEU scores are widely used in machine translation to measure the closeness of machine-translated output
to human translations. A higher BLEU score says similarity in the given inputs

5.2Qualitative Results
A qualitative assessment was conducted by analyzing the output translations of sample sentences from
both models. The following examples demonstrate the differences:

Example 1 (English to Hindi)

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 Hybrid Output: "Han introducido medidas innovadoras para mejorar la situación."
Analysis: The hybrid model’s translation uses "Han introducido" (present perfect) instead of "Ellos introdujeron"
(simple past), which is more contextually appropriate and sounds more fluent in Spanish.

1.Evaluation Metrics
1.Loss Function: Measures the model's performance during training, quantifying the discrepancy between
predicted and actual values. Lower values indicate better model accuracy in the prediction phase.
2.BLEU Score: Evaluates translation quality by comparing machine-translated text to one or more reference
translations. Higher scores indicate closer matches to human-level translation.
3.METEOR Score: This metric considers synonyms, stemming, and word order, making it a good measure for
semantic relevance in translations. Higher METEOR scores reflect improved semantic accuracy.
4.Accuracy: Measures the overall correctness of predictions made by the model on a sentence level.

5.4Comparative Results Analysis

Enhancing dataset size and refining hyperparameters, especially in the Hybrid model, could further improve
translation quality, particularly for idiomatic and domain- specific phrases. Additionally, exploring advanced
transformer-based embeddings in place of Word2Vec could further enhance context capture in the Hybrid
approach.
6.
Conclusion
This paper demonstrates the effectiveness of combining traditional SMT methods with neural network-based
word embeddings to enhance machine translation systems. The proposed hybrid system provides a
practical solution for improving translation quality, especially in low-resource language settings or domain-
specific applications. Future work will explore integrating more sophisticated embeddings and fine-tuning
NMT models for specific domains.
However, there are still many challenges to be addressed in this field, such as developing techniques to
handle low- resource languages, handling out-of-domain data, and improving the interpretability of NMT
models. Overall, improved NMT using NLP is a highly active area of research with significant potential for
practical applications in fields such as international business, diplomacy, and education.

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