The Future of Machine Translation: How NLP is Changing the Game

Dear Colleagues, 

This Special Issue on “The Future of Machine Translation: How NLP is Changing the Game” primally explores the various AI aspects involved in achieving seamless global communication and understanding. These aspects include, but are not limited to, multilingual models, real-time and lightweight models, contextual understanding, cultural and ethical considerations, sentiment analysis, and natural language processing (NLP).   

In recent years, NLP has made astonishing advancements due to the advent of the Transformer model, rapidly permeating many aspects of human daily life, industry operations, business models and so on. The extensive use of NLP technologies in our daily lives has increased the need for ethical considerations and bias mitigation. Meanwhile, maintaining data privacy and security has also become a crucial focus, especially in applications within specific fields such as medical, legal and business. The modern mobile lifestyle has stimulated the development of lightweight models to meet the requirement of real-time and device processing. Achieving human-like dialogues and translations urges the integration of psychology in NLP, necessitating more advanced research on emotion and sentiment recognition, adaption to language style and considerations of cultural and linguistic diversity. Additionally, handling issues in areas of low-resource languages, different accents, dialects and phonetic variations is also of paramount importance.

For this Special Issue, we welcome papers describing state-of-the-art approaches for all of the different aspects of AI for machine translation, with a special focus on approaches addressing the aforementioned topics:

• Privacy-preserving machine learning;
• Transfer learning, self-supervised learning for low-resource languages;
• Approaches integrating multi-cultural and multilingual information into machine learning networks;
• AI methodologies for bias detection or mitigation;
• Real-time and lightweight learning model for NLP;
• Adaptable speech recognition models for different accents and dialects;
• Approaches for emotion recognition and sentiment analysis.

Dr. Hueying Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• privacy-preserving in NLP
• transfer learning in NLP
• cross-cultural in NLP
• sentiment analysis
• ethics in NLP
• multilingual model