The proposed research will combine linguistic theory with recent advances in large language models to develop new datasets, annotation methodologies, evaluation benchmarks, and automatic evaluation approaches for metaphor-aware machine translation.
Abstract
Metaphorical language remains a major challenge for multilingual natural language processing because successful interpretation and translation require reasoning beyond literal lexical meaning. Existing research has largely investigated metaphor detection, machine translation, and translation evaluation as separate tasks, while little work has explored how these components can be integrated into a unified computational framework. This PhD proposal aims to develop an end-to-end framework for multilingual metaphor processing consisting of three complementary research directions: (1) robust metaphor detection across languages, (2) metaphor-oriented translation evaluation for both human assessment and automatic quality estimation, and (3) joint modelling that connects metaphor detection with translation evaluation. The proposed research will combine linguistic theory with recent advances in large language models to develop new datasets, annotation methodologies, evaluation benchmarks, and automatic evaluation approaches for metaphor-aware machine translation. The expected outcome is a unified framework that improves both the development and evaluation of multilingual NLP systems when processing figurative language.
A novel fusion-based Japanese-English translation system is presented to address the persistent challenges posed by heterogeneous, multi-domain text data in practical engineering environments. The study proposes an algorithmic framework designed to efficiently handle formal, informal, and technical language forms. It uses rule-based symbolic processing and advanced neural network translation models. The method aims to prepare complex Japanese input translation thru strict multistage data normalization, highly featured word segmentation, and adaptive contextual annotation. The core dual-path architecture dynamically controls the contribution of symbolic and neural components thru a context-aware weighting mechanism. This helps optimize translation fidelity based on the language characteristics and domain relevance of the input text. According to a comprehensive empirical evaluation, the system performs well in high variability domains and rare/ambiguous language patterns, and outperforms traditional neural network and symbolic baseline models in terms of efficiency and accuracy. Quantitative results show that the BLEU's appropriateness indicators, scores, and human fluency have been greatly improved. The technical design ensures industrial-grade throughput and scalability thru parallel reasoning and optimization. By combining symbolic language features and neural representation learning, the framework provides a reliable and adaptable solution to the limitations of current machine translation, and lays a technical foundation for advanced multilingual applications.
Na Qi· The 2026 International Confe...· 0 citations
Light is shed on the process-oriented nature of translation strategies and empirical evidence for integrating technological tools into translation pedagogy is offered.
Xuefeng Wu, Chenchen Liu· English Language Teaching· 0 citations
Semantic analysis has become a central challenge in natural language processing, driven by exponential growth in digitized textual data and the need for automated content processing across multiple applications including machine translation, text classification, sentiment analysis, and information retrieval. However, while semantic analysis methods are well-developed for resource-rich languages such as English, morphologically complex languages like Uzbek suffer from deficiencies in annotated corpora, lexical-semantic resources, and high-quality vector models – a gap amplified by governmental initiatives in digital economy development and national language technology advancement. This section grounds semantic analysis in the distributional semantics hypothesis principle that words exhibiting similar contexts possess similar meanings – thereby recasting the problem as a geometric challenge within continuous vector spaces. Two principal mathematical strategies are formalized: (1) prediction-based models (word2vec: CBOW/Skip-gram), which optimize context prediction objectives, and (2) count-based models (GloVe), which leverage global co-occurrence statistics through matrix factorization. Both project high-dimensional word co-occurrence relationships into low-dimensional dense vector spaces, enabling semantic analogy representation. For resource-scarce languages like Uzbek, cross-lingual embedding alignment (Procrustes optimization) enables semantic knowledge transfer from resource-rich languages, facilitating shared semantic spaces across the Turkic language family. The section concludes with formal problem specification: given vocabulary V and corpus C, semantic analysis is formalized as (1) a mapping problem preserving distributional properties, (2) an optimization problem minimizing loss through gradient-based methods, and (3) an evaluation problem assessing quality through semantic similarity, analogy, and downstream NLP task performance.
D. Akhmedjanova· Международный Журнал Теорети...· 0 citations
Results indicate that combining attention-based Transformer translation with practical deployment features — language detection, explainability, history management, and reporting — can deliver an accurate, scalable, and user-accessible multilingual translation platform.
Chinmaya Md, Supreetha Hd· International journal of tec...· 0 citations
Machine Translation has become one of the major application areas of Artificial Intelligence (AI) and Natural Language Processing (NLP), especially in multilingual countries like India. Although recent Neural Machine Translation systems have shown good performance for several language pairs, translation quality is still inconsistent for many Indian languages because of linguistic and structural differences between English and Indian language families. Most Indian languages are morphologically rich and contain flexible word order, complex agreement patterns, compound constructions, and context-dependent grammatical forms. Because of this, direct translation from English often produces structurally incorrect or semantically weak output. In many existing systems, the source sentence is passed to the translation model without sufficient linguistic analysis. As a result, ambiguity present in the source text propagates further during translation. This work focuses on the importance of linguistic enrichment before the translation stage. The proposed framework, named Unified Linguistic-Aware Pre-Parsing Framework, introduces a coordinated pre-processing layer for English-to-Indian Machine Translation (MT). A key contribution of this research is the development of a novel linguistically enriched intermediate representation that extends beyond conventional text normalization. By transforming noisy input text into linguistically enriched translation-ready representation, the proposed approach facilitates effective knowledge transfer to machine translation models, leading to improve contextual adequacy, linguistic fidelity, and overall translation performance. The framework combines multiple linguistic processing stages including POS tagging, NE detection, clause boundary analysis, contextual token handling, syntactic structure preparation, and morphology-related processing. Instead of executing these modules independently, the proposed system allows interaction between lexical, syntactic, and morphological information during analysis. This helps reduce structural ambiguity and improves sentence-level interpretation before translation begins. The need for such a framework becomes more relevant in the context of Indian languages where morphology and grammatical relations carry significant semantic information. This framework is especially relevant for Indian languages, where semantic information is often encoded through morphological variations and grammatical dependencies. The proposed framework can be effectively integrated with both conventional machine translation architectures and modern large language models. The overall study highlights how classical linguistic analysis can still play an important role in improving multilingual AI systems for Indian languages.
Prashant Chaudhary, Pavan Kurariya, Jahnavi Bodhankar et al.· NLP & Big Data· 0 citations
It is argued that NLP becomes most valuable for the Iraqi humanities when computational methods are combined with cultural knowledge, philological care, translation ethics, and close literary interpretation.
Tetyana Panyok· Iraqi Literary and Cultural...· 0 citations