Symmetry and Asymmetry in Natural Language Processing
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 15
Special Issue Editors
Interests: natural language processing; social media computing; large language models; multimodality
Interests: information extraction; text mining; knowledge graph
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Natural Language Processing (NLP), as a significant branch of artificial intelligence, is dedicated to enabling computers to understand, generate, and interact with human language. In recent years, NLP has experienced rapid development. Typical NLP tasks include dialogue systems, machine translation, text summarization, information extraction, and topic classification, among others. In these tasks, data and models often exhibit characteristics of symmetry and asymmetry. For example, machine translation generally requires semantic symmetry between the source and target languages, ensuring consistent meaning transmission across different languages. However, it exhibits textual asymmetry due to differences in grammatical structures and expressive methods between languages. Similarly, in text entailment tasks, Siamese network structures typically achieve better performance.
In recent years, with significant advancements in data scale and computational resources, Large Language Models (LLMs) have emerged and demonstrated exceptional performance across numerous NLP tasks, profoundly transforming the research paradigms for NLP tasks, models, and data characterized by symmetry or asymmetry. Therefore, this Special Issue aims to introduce new methods, frameworks, and theories for addressing NLP tasks, data, and models with symmetric and asymmetric characteristics in the context of the era of LLMs.
We are soliciting submissions on a wide range of topics related to NLP (research and review articles), with a particular focus on recent research on NLP tasks with symmetry and asymmetry. Specifically, we welcome submissions including but not limited to the following areas:
- Solutions for symmetric/asymmetric tasks, models, and data in NLP;
- New models, algorithms, and frameworks for NLP tasks;
- Applications of LLMs in NLP tasks;
- Applications of neural network models in NLP tasks;
- Research on interpretability and model transparency in NLP;
- Multimodal tasks in NLP;
- New tasks and new data reflecting symmetry and asymmetry in NLP.
Dr. Yang Li
Dr. Junwen Duan
Guest Editors
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. Symmetry is an international peer-reviewed open access monthly 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.
Keywords
- deep learning
- natural language processing
- large language model
- multimodal model
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