Andong Chen, Yuchen Song, Kehai Chen, Xuefeng Bai, Muyun Yang, Liqiang Nie, Jie Liu, Tiejun Zhao, Min Zhang. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Large language models (LLMs) have shown remarkable performance in general translation tasks.However, the increasing demand for high-quality translations that are not only adequate but also fluent and elegant.To assess the extent to which current LLMs can meet these demands, we introduce a suitable benchmark (PoetMT) for translating classical Chinese poetry into English.This task requires not only adequacy in translating culturally and historically significant content but also a strict adherence to linguistic fluency and poetic elegance.Our study reveals that existing LLMs fall short of this task.To address these issues, we propose RAT, a Retrieval-Augmented machine Translation method that enhances the translation process by incorporating knowledge related to classical poetry.Additionally, we propose an automatic evaluation metric based on GPT-4, which better assesses translation quality in terms of adequacy, fluency, and elegance, overcoming the limitations of traditional metrics.Our dataset and code will be made available 1 .

Legal Question Answering (LQA) is a promising artificial intelligence application with high practical value. A professional and effective legal question answering (QA) agent can assist in reducing the workload of lawyers and judges, and help to achieve judicial accessibility. However, the NLP community lacks a large-scale LQA dataset with high quality, making it difficult to develop practical data-driven LQA agents. To tackle this bottleneck, this work presents EQUALS, a well-annotated real-world dataset for lEgal QUestion Answering via reading Chinese LawS. EQUALS contains 6,914 {question, article, answer} triplets as well as a pool of articles of laws that covers 10 different collections of Chinese Laws. Questions and the corresponding answers in EQUALS are collected from a professional law consultation forum. More importantly, the exact spans of law articles are annotated by senior law students as the answers. In this way, we could assure the quality and professionalism of EQUALS. Furthermore, this work proposes a QA framework that encompasses a law article retrieval module and a machine reading comprehension module for extracting accurate answers from the law article. We conduct thorough experiments with representative baselines on EQUALS, and the results indicate that EQUALS is a challenging question answering task. To the best of our knowledge, EQUALS is the largest real-world LQA dataset which shall significantly promote the research of LQA tasks. The work has been open-sourced and is available at: https://github.com/andongBlue/EQUALS.

Learning the representation of a knowledge graph is critical to the field of natural language processing. There is a lot of research for English knowledge graph representation. However, for the low-resource languages, such as Tibetan, how to represent sparse knowledge graphs is a key problem. In this article, aiming at scarcity of Tibetan knowledge graphs, we extend the Tibetan knowledge graph by using the triples of the high-resource language knowledge graphs and Point of Information map information. To improve the representation learning of the Tibetan knowledge graph, we propose a joint model to merge structure and entity description information based on the Translating Embeddings and Convolution Neural Networks models. In addition, to solve the segmentation errors, we use character and word embedding to learn more complex information in Tibetan. Finally, the experimental results show that our model can make a better representation of the Tibetan knowledge graph than the baseline.