Project Background:
Neural Machine Translation (NMT) represents a paradigm shift in the field of machine translation, leveraging deep learning techniques to achieve state-of-the-art results. The NMT is centered on addressing complexities of language translation tasks by employing neural networks to model the entire translation process. Unlike traditional statistical machine translation models that relied on hand-crafted features and rule-based systems, NMT systems learn directly from data, allowing them to capture intricate linguistic patterns and dependencies. This project work aims to push the boundaries of NMT by exploring advanced architectures such as transformer models, attention mechanisms, and multi-task learning strategies. By harnessing the power of NMT, the project seeks to improve translation accuracy, fluency, and the ability to handle diverse language pairs and domains.
Problem Statement
- Handling diverse linguistic structures and nuances across languages poses a challenge in Neural Machine Translation (NMT).
- NMT systems may struggle to adapt effectively to specific domains, resulting in decreased translation quality.
- Limited availability of parallel data for low-resource languages hinders the performance of NMT models.
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NMT models may encounter difficulties in translating long sentences accurately due to context dependencies.
- Handling noisy input, such as spelling errors or grammatical inconsistencies, can impact the accuracy of NMT outputs.
Aim and Objectives
- Enhance the quality and fluency of Neural Machine Translation (NMT) outputs.
- Develop advanced NMT architectures, such as transformer models, to improve translation accuracy.
- Address domain adaptation challenges by incorporating domain-specific knowledge into NMT systems.
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Explore techniques for handling low-resource languages in NMT, such as data augmentation and transfer learning.
- Improve the handling of long sentences in NMT by optimizing attention mechanisms and memory management.
- Investigate methods for handling noisy input and improving robustness in NMT outputs.
- Enhance multi-lingual translation capabilities of NMT models for diverse language pairs.
Contributions to Neural Machine Translation
- Proposed novel attention mechanisms that improve the quality and fluency of NMT outputs.
- Developed domain-specific adaptation techniques to enhance the performance of NMT systems in specialized domains.
- Introduced data augmentation strategies for low-resource languages, improving translation quality and coverage.
- Enhanced NMT robustness by incorporating techniques to handle noisy input and improve overall model reliability.
Deep Learning Algorithms for Neural Machine Translation
- Transformer
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Recurrent Neural Network (RNN)-based Encoder-Decoder
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Long Short-Term Memory (LSTM)
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Gated Recurrent Unit (GRU)
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Attention Mechanism
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Sequence-to-Sequence (Seq2Seq) Models
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Convolutional Sequence-to-Sequence (ConvS2S) Models
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BERT (Bidirectional Encoder Representations from Transformers)
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MarianMT
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Fairseq
Datasets for Neural Machine Translation
- WMT (Workshop on Machine Translation) datasets
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IWSLT
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MultiUN Corpus
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TED Talks Translations
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News Commentary
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OpenSubtitles
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JW300
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UNPC (United Nations Parallel Corpus)
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Tatoeba
Performance Metrics
- BLEU (Bilingual Evaluation Understudy)
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METEOR (Metric for Evaluation of Translation with Explicit ORdering)
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TER (Translation Edit Rate)
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NIST (NIST Automated Evaluation of Machine Translation)
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ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
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ChrF (Character n-gram F-score)
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CIDEr (Consensus-based Image Description Evaluation)
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GLEU (Google-BLEU)
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WER (Word Error Rate)
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PER (Position-independent Error Rate)
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
