naveen-1609/talk_beyond

Talk Beyond

Abstract

Our project focuses on delivering a highly accurate English to French translation service. We aim to conduct a comprehensive comparison of different machine learning models, including sequence-to-sequence, bidirectional RNN, and Transformers, to evaluate their translation efficacy. Each model will be meticulously trained on a robust bilingual dataset, ensuring a sustainable approach to capturing the intricacies of language translation. Our goal is to identify which model not only performs with the highest accuracy but also integrates seamlessly for real-world application. We anticipate that our findings will contribute to the development of more sophisticated and nuanced language translation tools, paving the way for better cross-cultural communication.

GitHub link

Introduction

In an increasingly interconnected world, the demand for accurate and efficient language translation services is ever-growing. This project, titled "Talk Beyond," is dedicated to enhancing English to French translation by leveraging cutting-edge machine learning models. Our aim is to dissect and evaluate the performance of three distinct architectures: sequence-to-sequence models, bidirectional Recurrent Neural Networks (RNNs), and the revolutionary Transformer models.

The choice of these models is informed by their proven capabilities in handling various aspects of language processing, from basic translation tasks to the maintenance of context in complex sentence structures. Through a meticulous training regime, each model will be exposed to a comprehensive bilingual dataset. This dataset has been curated not only for its extensive vocabulary and complex sentence constructs but also for its reflection of contemporary language use in both English and French.

Our methodology is twofold: we first aim to train each model to achieve a high degree of accuracy on a standard set of translation tasks. Following this, we will delve into real-world application scenarios to test the adaptability of each model. The ultimate goal is to discern which model—or combination of models—provides the most seamless translation in practical settings, without sacrificing the nuances that characterize human language.

The significance of this research lies not only in the advancement of translation technology but also in its potential to facilitate smoother cross-cultural communication. By pushing the boundaries of what machine learning can achieve in the realm of language translation, "Talk Beyond" stands to be a pivotal step toward a future where language barriers are significantly reduced, if not entirely overcome.

Contribution

• Naveen Venkat Yelamanchili: Project initial draft, Sequence to Sequence model, Bidirectional RNN
• Lokesh Lochan Dharmavaram: Transformers, Project Slides, GitHub Management

Method

Our approach to creating a nuanced English to French translation service involves three distinct machine learning architectures, each with unique attributes tailored to overcome the challenges of language translation. Read more about our method.

Data

Our Dataset contains more than 20 Million records with two columns, one for English and the other for French. We would like to segregate our data into 5 or more batches and then train and test the model using one batch and then, we train our models on other batches by evaluating its performance throughout the entire process.

Data set link

Tools & Technologies

We utilized TensorFlow and Keras for model development. The project was executed on Google Colab, providing necessary GPU support. We might also use GCP for evaluating the model performance.

Experiments

In alignment with our multifaceted method, our experimental phase was meticulously designed to evaluate and refine each model: the Sequence-to-Sequence with Attention, Bidirectional RNN, and Transformer models.

Throughout these experiments, we meticulously recorded the efficiency of each model. Parameters were fine-tuned iteratively, with the goal of optimizing each model's performance not only in terms of translation accuracy but also in terms of operational viability for real-world applications.

Results

(Currently working on the model part, will update once it's done)

Problems/Issues

Data Volume: Since we have a huge dataset, it took a lot of time in compiling each stage of this project.

(as of now)

Conclusion

(Yet to be concluded)