YNU - HPCC at S em E val-2025 Task 2: Local Cache and Online Retrieval-Based method for Entity-Aware Machine Translation (2025)

Abstract: This paper presents methods for {textbf{SemEval-2025 Task 11}} on text-based emotion detection across three tracks: Multi-label Emotion Detection, Emotion Intensity Prediction, and Cross-lingual Emotion Detection. We apply approaches such as supervised fine-tuning, preference-based reinforcement learning, and few-shot learning to enhance performance. Our combined strategies result in improved accuracy, particularly in multi-label and cross-lingual emotion detection, demonstrating the effectiveness of these methods in diverse linguistic settings.

## introduction the definition of named entities was first proposed by beth m. sundheim in 1995 @xcite . named entities typically refer to entities in a text that have a specific identity or name. in machine translation tasks, named entities may be related to language and cultural differences. the meaning of the same named entity can vary significantly in different contexts. the translation result may be unsatisfactory if the model fails to correctly identify potential named entities in a sentence and translate them in the context. in the past, people used methods such as regular matching, traditional machine learning models like svm @xcite , and context-dependent models like lstm for translating sentences with named entities. this paper proposes four methods, respectively, based on traditional machine translation models and llm-based models, aiming to improve the translation accuracy of sentences with named entities and explore the capabilities of llms. this study proposes a local cache and online retrieval-based method for translating sentences containing named entities, which consists of two modules. the ner module is primarily used to accurately identify named entities in the sentence, preparing for the subsequent translation task. the translation module is mainly responsible for integrating the ne translation results and translating the entire sentence in context. experimental results show that traditional mt models have speed and deployment cost advantages, while llm-based models outperform translation accuracy and contextual coherence. the rest of the paper is organized as follows: first, our team propose four methods to improve the performance of ea-mt based on the two modules mentioned above. then we test them across all ten languages. finally, we will analyze the experimental results. ## related work named entity recognition (ner) is a fundamental task in natural language processing (nlp), with applications in social media @xcite , news @xcite , e-commerce @xcite , nested medical ner @xcite and other fields. ner in the past was performed using methods such as regular expression matching, traditional machine learning models like support vector machines (svm), conditional random fields (crf) @xcite , maximum entropy models (maxent) @xcite , recurrent neural networks (rnn) @xcite , and long short-term memory networks (lstm). although these methods were effective in specific domains, the training process was cumbersome, and the ability to recognize context remained weak. with the introduction of transformers and the emergence of various pretrained models @xcite , recent advancements in pre-trained models, such as transformerbased model in name entity recognition @xcite , bert @xcite , tacl-bert, and nezha @xcite , have significantly improved ner performance. in semeval 2022 task 11 @xcite , impressive results were achieved by two teams: one proposed a knowledgebased ner system, while the other enhanced their model's performance by utilizing a gazetteer constructed from wikidata. in semeval 2023 task 2 @xcite , one of the top-performing teams approached ner as a sequence labeling problem. these methods have performed well in their respective tasks and provided valuable insights for our upcoming task. ## system description this section introduces four methods implemented in task 2 @xcite and analyzes how each works. ## results in the semeval 2025 task 2: ea-mt, the system performed translations for all 10 languages. the model ranked fifth without using wikidata ids or information, fifth without rag, and seventh without fine-tuning. the final overall ranking was 11th. as shown in credibility of the outcomes, facilitating subsequent debugging and improvement. the comet and m-eta and overall scores for each method are shown in ## conclusion this study implements four methods for accurately translating sentences containing named entities in this shared task. a translation method based on traditional mt models was developed, offering speed, ease of deployment, and a certain level of named entity recognition. multiple llms were combined for models based on llms, focusing on named entity recognition and translation. a named entity dictionary was utilized to improve the translation accuracy of entities. additionally, the langchain framework was used to test the performance of llms in generating reasoning trajectories and taskspecific actions in the translation of named entities. our experiments show that, with an additional named entity dictionary, llms can be effectively used for cross-lingual content translation involving unknown or complex named entities. for future work, the tool functions in langchain to assist llms in translation, and it is expected that finetuning and rag techniques will be combined to enhance the performance of llms in ea-mt further.