Project Background:
Multimodal Machine Translation (MMT) is situated at the intersection of two critical fields, machine translation and computer vision. It emerges from the recognition that traditional machine translation systems primarily focus on translating text are limited in their ability to capture the full context and richness of multimodal content that combines text with visual elements, such as images or videos. In todays interconnected digital world, where multimedia content is ubiquitous across various platforms that encompass both linguistic and visual information has grown significantly. MMT seeks to address this demand by leveraging advances in deep learning, neural networks, and natural language processing to provide more holistic translations. MMT is motivated by the need to break down language barriers, enhance cross-cultural communication, improve accessibility, and offer a deeper understanding of content by integrating and aligning textual and visual data sources.
Problem Statement
- To ensure high-quality translation for each modality, which includes maintaining linguistic accuracy, fluency, and context preservation across text, image captions, and spoken language while also handling various forms of text-image-speech interactions.
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Must address the alignment and synchronization of content across modalities, that ensuring the translated text corresponds correctly to the visual and auditory components.
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Developing MMT models is inherently complex due to the diverse data types involved, need to handle textual data, visual information and possibly audio data, requiring the integration of various neural network architectures.
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Creating appropriate evaluation metrics for MMT poses a challenge. Traditional translation evaluation metrics may not fully capture the quality of translated content when dealing with multiple modalities.
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Handling multimodal content introduces privacy and security concerns. MMT systems must ensure the secure handling of sensitive information in text, images, and speech during translation.
Aim and Objectives
- Enable accurate translation of content that combines multiple modalities, enhancing communication and information consumption.
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Ensure systems produce translations that are of high linguistic quality, maintaining accuracy, fluency, and context preservation across diverse modalities.
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Develop techniques to align and synchronize content across different modalities, ensuring that translated text corresponds correctly to visual and auditory components.
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Create evaluation metrics specifically tailored to MMT to assess translation quality accurately across multiple modalities.
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Implement robust security measures to protect sensitive information in multimodal content during translation, addressing privacy and security concerns.
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Explore methods for collaborative translation, where multiple users contribute to the translation process, potentially improving the quality and accuracy of translations.
Contributions to Multimodal Machine Translation
1. Developing innovative neural network architectures that effectively integrate text, image, and speech data to improve translation quality in MMT.
2. Creating and curating large, diverse multimodal datasets to train and evaluate MMT models, addressing the scarcity of resources.
3. Advancing transfer learning techniques to adapt pre-trained models for MMT, enabling more efficient model development.
4. Proposing and refining evaluation metrics specifically tailored to assess the quality of multimodal translations.
5. Developing robust privacy and security mechanisms to protect sensitive information during multimodal translation.
6. Advancing techniques to seamlessly translate between multiple languages and integrate various modalities within a single translation system.
7. Investigating the broader societal impact and ethical considerations associated with MMT, including issues related to accessibility and inclusivity.
Deep Learning Algorithms for Multimodal Machine Translation
- Vision-Transformer (ViT)
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Speech-Transformer
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T2T (Text-to-Text) models
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Multimodal fusion networks
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Parallel Data Augmentation
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Reinforcement Learning for MMT
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Sequence-to-sequence models with attention
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Multimodal autoencoders
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Recurrent Neural Networks (RNNs) for multimodal sequences
Datasets for Multimodal Machine Translation
- Multi30k
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MultiUN
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Multi30k Translations
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MMID
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How2
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IAPR-TC12
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CzEng 1.6
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TED Multimodal Translation Corpus
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MLS14
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ESP-Parl
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MuST-C
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UN-corpus
Performance Metrics
- BLEU
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METEOR
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ROUGE
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CIDEr
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SPICE
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METEOR
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FrechetBERT
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Multimodal BLEU
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Multimodal METEOR
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Multimodal TER
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Visual-METEOR
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Visual-TER
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Visual-ROUGE
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Simultaneous translation evaluation metrics
