With this 100% online Postgraduate diploma, you will acquire advanced skills in the use of Artificial Intelligence tools and technologies, improving efficiency and accuracy in translation and interpreting” 

The application of Artificial Intelligence techniques in machine translation has reached new heights thanks to advances in Neural Machine Translation (NMT) models. As the architecture of these models is improved, techniques such as transfer learning and contextual attention are integrated, resulting in higher translation quality and broader support for multiple languages and domains.

This is how this Postgraduate diploma was created, in which professionals will learn about the different approaches to translation and interpretation, from classical models to those based on Artificial Intelligence, as well as their relevance in natural language processing. In addition, they will acquire the necessary skills to implement advanced techniques to facilitate machine translation and improve the understanding of linguistic nuances.

They will also develop skills to evaluate the quality of the translations generated, using specific metrics and indicators to ensure the accuracy and efficiency of the results. This will not only increase productivity, but will also allow experts to adapt to a dynamic and constantly evolving work environment, where immediacy is crucial.

Finally, students will be able to integrate linguistic resources and databases into these platforms, which will enable them to improve the consistency and quality of their translations. This will not only foster familiarity with current technologies, but will also prepare them to face future challenges in the field of machine translation.

In this way, TECH has developed a comprehensive, fully online program, which only requires an electronic device with an Internet connection to access all educational materials. This eliminates problems such as travel to a physical location and the need to follow a rigid schedule. Additionally, it will be based on the innovative Relearning methodology, focused on the continuous repetition of key concepts to promote optimal assimilation of the contents.

You will be able to assess the quality of translations in real time and integrate linguistic resources, optimizing your workflow and increasing productivity and consistency in your projects” 

This Postgraduate diploma in Application of Artificial Intelligence Techniques for Machine Translation contains the most complete and up-to-date program on the market. The most important features include: 

• The development of case studies presented by experts in Artificial Intelligence applied to Translation and Interpreting
• The graphic, schematic, and practical contents with which they are created, provide practical information on the disciplines that are essential for professional practice
• Practical exercises where self-assessment can be used to improve learning.
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

You will become familiar with linguistic resources and databases, equipping you to face the challenges of the translation field, using tools that allow them to work more efficiently and effectively” 

The program’s teaching staff includes professionals from the field who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the course. For this purpose, students will be assisted by an innovative interactive video system created by renowned experts in the field of educational coaching with extensive experience.

You will acquire knowledge of the evolution of linguistic models underpinning translation and interpreting, from classical approaches to innovations based on Artificial Intelligence"

You will be able to critically evaluate the quality of translations, using specific metrics and indicators, ensuring that translations meet the standards required in professional environments"

This Postgraduate diploma will cover a wide range of contents that will train professionals in the use of advanced technologies for translation and natural language processing. Therefore, they will delve into linguistic models, exploring both classical and modern approaches based on Artificial Intelligence, which will allow them to understand the theoretical foundations that support machine translation. In addition, real-time translation tools will be addressed, where the quality of translations will be evaluated through specific metrics. 

You will deepen your knowledge of AI-assisted translation platforms, thus optimizing the professional workflow through the integration of linguistic resources and databases” 

Module 1. Linguistic Models and AI Application

1.1. Classical Models of Linguistics and their Relevance to AI

1.1.1. Generative and Transformational Grammar
1.1.2. Structural Linguistic Theory
1.1.3. Formal Grammar Theory
1.1.4. Applications of Classical Models in AI

1.2. Probabilistic Models in Linguistics and Their Application in AI

1.2.1. Hidden Markov Models (HMM)
1.2.2. Statistical Language Models
1.2.3. Supervised and Unsupervised Learning Algorithms
1.2.4. Applications in Speech Recognition and Text Processing

1.3. Rule-Based Models and Their Implementation in AI. GPT

1.3.1. Formal Grammars and Rule Systems
1.3.2. Knowledge Representation and Computational Logic
1.3.3. Expert Systems and Inference Engines
1.3.4. Applications in Dialog Systems and Virtual Assistants

1.4. Deep Learning Models in Linguistics and Their Use in AI

1.4.1. Convolutional Neural Networks for Text Processing
1.4.2. Recurrent Neural Networks and LSTM for Sequence Modeling
1.4.3. Attention Models and Transformers. APERTIUM
1.4.4. Applications in Machine Translation, Text Generation and Sentiment Analysis.

1.5. Distributed Language Representations and Their Impact on AI

1.5.1. Word Embeddings and Vector Space Models
1.5.2. Distributed Representations of Sentences and Documents
1.5.3. Bag-of-Words Models and Continuous Language Models
1.5.4. Applications in Information Retrieval, Document Clustering and Content Recommendation

1.6. Machine Translation Models and Their Evolution in AI. Lilt

1.6.1. Statistical and Rule-Based Translation Models
1.6.2. Advances in Neural Machine Translation
1.6.3. Hybrid Approaches and Multilingual Models
1.6.4. Applications in Online Translation and Content Localization Services

1.7. Sentiment Analysis Models and Their Usefulness in AI

1.7.1. Sentiment Classification Methods
1.7.2. Detection of Emotions in Text
1.7.3. Analysis of User Opinions and Comments
1.7.4. Applications in Social Networks, Analysis of Product Opinions and Customer Service

1.8. Language Generation Models and Their Application in AI. TransPerfect Globallink

1.8.1. Autoregressive Text Generation Models
1.8.2. Conditioned and Controlled Text Generation
1.8.3. GPT-Based Natural Language Generation Models
1.8.4. Applications in Automatic Typing, Text Summarization, and Intelligent Conversation

1.9. Speech Recognition Models and Their Integration in AI

1.9.1. Audio Feature Extraction Methods
1.9.2. Speech Recognition Models Based on Neural Networks
1.9.3. Improvements in Speech Recognition Accuracy and Robustness
1.9.4. Applications in Virtual Assistants, Transcription Systems and Speech-based Device Control

1.10. Challenges and Future of Linguistic Models in AI

1.10.1. Challenges in Natural Language Understanding
1.10.2. Limitations and Biases in Current Linguistic Models
1.10.3. Research and Future Trends in AI Linguistic Modeling
1.10.4. Impact on Future Applications such as General Artificial Intelligence (AGI) and Human Language Understanding. SmartCAt

Module 2. AI and Real-Time Translation

2.1. Introduction to Real-Time Translation with AI

2.1.1. Definition and Basic Concepts
2.1.2. Importance and Applications in Different Contexts
2.1.3. Challenges and Opportunities
2.1.4. Tools such as Fluently or Voice Tra

2.2. Artificial Intelligence Fundamentals in Translation

2.2.1. Brief Introduction to Artificial Intelligence
2.2.2. Specific Applications in Translation
2.2.3. Relevant Models and Algorithms

2.3. AI-Based Real-Time Translation Tools

2.3.1. Description of the Main Tools Available
2.3.2. Comparison of Functionalities and Features
2.3.3. Use Cases and Practical Examples

2.4. Neural Machine Translation (NMT) Models. SDL Language Cloud

2.4.1. Principles and Operation of NMT Models
2.4.2. Advantages over Traditional Approaches
2.4.3. Development and Evolution of NMT Models

2.5. Natural Language Processing (NLP) in Real-Time Translation. SayHi TRanslate

2.5.1. Basic NLP Concepts Relevant to Translation
2.5.2. Preprocessing and Post-Processing Techniques
2.5.3. Improving the Coherence and Cohesion of the Translated Text

2.6. Multilingual and Multimodal Translation Models

2.6.1. Translation Models that Support Multiple Languages
2.6.2. Integration of Modalities such as Text, Speech and Images
2.6.3. Challenges and Considerations in Multilingual and Multimodal Translation

2.7. Quality Assessment in Real-Time Translation with AI

2.7.1. Translation Quality Assessment Metrics
2.7.2. Automatic and Human Evaluation Methods. iTranslate Voice
2.7.3. Strategies to Improve Translation Quality

2.8. Integration of Real-Time Translation Tools in Professional Environments

2.8.1. Use of Translation Tools in Daily Work
2.8.2. Integration with Content Management and Localization Systems
2.8.3. Adaptation of Tools to Specific User Needs

2.9. Ethical and Social Challenges in Real-Time Translation with AI

2.9.1. Biases and Discrimination in Machine Translation
2.9.2. Privacy and Security of User Data
2.9.3. Impact on Linguistic and Cultural Diversity

2.10. Future of AI-Based Real-Time Translation. Applingua

2.10.1. Emerging Trends and Technological Advances
2.10.2. Future Prospects and Potential Innovative Applications
2.10.3. Implications for Global Communication and Language Accessibility

Module 3. AI-Assisted Translation Tools and Platforms

3.1. Introduction to AI-Assisted Translation Tools and Platforms

3.1.1. Definition and Basic Concepts
3.1.2. Brief History and Evolution
3.1.3. Importance and Benefits in Professional Translation

3.2. Main AI-Assisted Translation Tools

3.2.1. Description and Functionalities of the Leading Tools on the Market
3.2.2. Comparison of Features and Prices
3.2.3. Use Cases and Practical Examples

3.3. Professional AI-Assisted Translation Platforms. Wordfast

3.3.1. Description of Popular AI-Assisted Translation Platforms
3.3.2. Specific Functionalities for Translation Teams and Agencies
3.3.3. Integration with Other Project Management Systems and Tools

3.4. Machine Translation Models Implemented in AI-Assisted Translation Tools

3.4.1. Statistical Translation Models
3.4.2. Neural Translation Models
3.4.3. Advances in Neural Machine Translation (NMT) and Its Impact on AI-Assisted Translation Tools

3.5. Integration of Linguistic Resources and Databases in AI-Assisted Translation Tools

3.5.1. Using Corpus and Linguistic Databases to Improve Translation Accuracy
3.5.2. Integrating Specialized Dictionaries and Glossaries
3.5.3. Importance of Context and Specific Terminology in AI-Assisted Translation

3.6. User Interface and User Experience in AI-Assisted Translation Tools

3.6.1. User Interface Design and Usability
3.6.2. Customization and Preference Settings
3.6.3. Accessibility and Multilingual Support on AI-Assisted Translation Platforms

3.7. Quality Assessment in AI-Assisted Translation

3.7.1. Translation Quality Assessment Metrics
3.7.2. Machine vs. Human Evaluation
3.7.3. Strategies to Improve the Quality of AI-Assisted Translation

3.8. Integration of AI-Assisted Translation Tools into the Translator's Workflow

3.8.1. Incorporation of AI-Assisted Translation Tools into the Translation Process
3.8.2. Optimizing Workflow and Increasing Productivity
3.8.3. Collaboration and Teamwork in AI-Assisted Translation Environments

3.9. Ethical and Social Challenges in the Use of AI-Assisted Translation Tools

3.9.1. Biases and Discrimination in Machine Translation
3.9.2. Privacy and Security of User Data
3.9.3. Impact on the Translation Profession and on Linguistic and Cultural Diversity

3.10. Future of AI-Assisted Translation Tools and IA. Wordbee

3.10.1. Emerging Trends and Technological Developments
3.10.2. Future Prospects and Potential Innovative Applications
3.10.3. Implications for Training and Professional Development in the Field of Translation

You will gain a comprehensive and up-to-date vision that will enable you to develop innovative solutions to improve professional development and job satisfaction”