Application of Symmetry in Natural Language Processing

Dear Colleagues,

Natural language processing (NLP) has witnessed remarkable progress through advances in machine learning and deep learning, yet a fundamental computational principle, such as symmetry, remains underexplored. Recent evidence demonstrates that symmetry operates at multiple linguistic scales, from word embeddings achieving superior similarity prediction to sentence-level semantic parsing using symmetry group theory. It reveals its task-dependent utility across NLP applications, including machine translation, information extraction, sentiment analysis, dialogue systems, and question answering.

Symmetry in NLP manifests as structural, semantic, or representational equivalence, exemplified by meaning preservation across paraphrases, bidirectional mappings between languages, and invariant representations under syntactic transformations. Empirical studies show that symmetric pattern-based models excel at capturing reciprocal relationships and predicate symmetry (e.g., “married”, “collaborated”), achieving near-ceiling performance when combined with linguistic features. Conversely, asymmetry proves equally fundamental for modeling hierarchical semantic structures, lexical substitutability, entailment, information flow, and discourse structure. Hybrid approaches integrating symmetric features with neural architectures demonstrate systematic improvements over pure neural methods, indicating complementary rather than redundant benefits.

The emergence of large language models (LLMs) and transformer architectures has created new opportunities for explicit symmetry modeling within neural systems. While these models implicitly encode linguistic regularities through large-scale pretraining, explicit symmetry integration offers distinct advantages such as computational efficiency through shallow parsing requiring only POS information, unsupervised pattern extraction requiring no labeled examples, and enhanced interpretability by reconciling surface linguistic features with contextual information. Critical questions have emerged about how symmetry can be explicitly incorporated into neural architectures, loss functions, data representations, transformations, regularization, or constraints. Evidence suggests that combined approaches integrating symmetric patterns with contextualized representations systematically outperform either method alone. Addressing these questions is essential for improving model robustness, generalization, and fairness, particularly in multilingual, low-resource, and cross-domain settings where data efficiency becomes paramount.

This Special Issue on “Application of Symmetry in Natural Language Processing” provides a dedicated forum for advancing both theoretical foundations and practical implementations of symmetry-related concepts. We seek original research that investigates symmetry at multiple linguistic scales (word-level, sentence-level, document-level); explores computational formalizations (bidirectional equivalence, transformation preservation, group-theoretic frameworks); and develops novel implementation methods, including symmetric neural architectures, loss functions, data representations, and regularization techniques. Of particular interest are contributions that address the task-dependent nature of symmetry, when symmetric formulations excel (reciprocal relationships, paraphrase identification) versus when asymmetric frameworks prove superior (substitutability, hierarchical semantics), and hybrid approaches that integrate explicit symmetry modeling with contextualized representations to achieve complementary benefits.

We invite high-quality original research articles and review papers covering, but not limited to, the following topics:

• Symmetry and asymmetry in word embeddings, semantic representations, and distributional models.
• Mathematical and computational formalizations of symmetry (group theory, bidirectional equivalence, transformation preservation).
• Symmetric neural architectures, loss functions, and regularization techniques for NLP.
• Symmetry in semantic parsing, predicate inference, and reciprocal relationship detection.
• Word similarity prediction and lexical substitutability using symmetric and asymmetric frameworks.
• Paraphrase identification and meaning-preserving transformations.
• Symmetry in multilingual, cross-lingual, and low-resource NLP with limited training data.
• Hybrid models integrating explicit symmetry features with contextualized representations (BERT, GPT, LLMs).
• Computational efficiency through symmetry-based shallow parsing and unsupervised pattern extraction.
• Symmetry and invariance in self-supervised learning and representation learning.
• Interpretability and explainability through symmetry-based linguistic feature analysis.
• Evaluation frameworks comparing symmetric vs. asymmetric approaches across NLP tasks.
• Multimodal NLP tasks involving symmetric and asymmetric structures.
• Geometric patterns in text and multilingual NLP.
• Geometry of multilingual language model representations.

We anticipate that this Special Issue will catalyze a systematic investigation of symmetry’s role in NLP, moving beyond incidental mentions to become core methodological components. By bringing together empirical studies, theoretical frameworks, case studies, and systematic reviews, we aim to establish when and why symmetry principles enhance NLP performance, clarify the complementary roles of symmetric and asymmetric formulations across linguistic scales, and advance practical methods for integrating symmetry modeling with modern neural architectures. This interdisciplinary dialogue will bridge linguistic theory, mathematical foundations, and computational implementations to develop more efficient, interpretable, and theoretically grounded NLP systems.

Prof. Dr. Sunday Olusegun Ojo
Prof. Dr. Olawande Daramola
Guest Editors

Dr. Tebatso Gorgina Moape
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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.

• natural language processing
• symmetry and asymmetry
• word embeddings
• semantic parsing
• representation learning
• large language models
• hybrid neural architectures
• multilingual NLP
• low-resource NLP
• predicate inference
• lexical substitutability
• interpretability
• computational efficiency
• paraphrase detection