@inproceedings{zhu-etal-2025-efsa,
    title = "{EFSA}-{CLC}: Enhancing Zero-shot Entity-level Financial Sentiment Analysis with Cross-lingual Collaboration",
    author = "Zhu, Senbin  and
      Liu, Hongde  and
      He, Chenyuan  and
      Jia, Yuxiang",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.160/",
    pages = "2993--3003",
    ISBN = "979-8-89176-298-5",
    abstract = "Entity-level sentiment analysis is becoming increasingly important in the context of diverse financial texts, and large language models demonstrate significant potential under zero-shot settings. While it is well recognized that different languages embody distinct cognitive patterns, the use of multilingual capabilities in large language models to enable cross-lingual collaborative reasoning in the financial domain remains insufficiently studied. To address this, we propose a Cross-Lingual Collaboration (CLC) method: first, financial texts are aligned from one language to another based on semantic and syntactic structures, enabling the model to capture complementary linguistic features. Then, we integrate sentiment analysis results from both languages through redundancy removal and conflict resolution, enhancing the effectiveness of cross-lingual collaboration. Our experiments cover seven languages from three language families, including six UN official languages, and evaluate CLC on two English datasets and one Chinese dataset. Results show that multilingual collaboration improves sentiment analysis accuracy, especially among linguistically similar languages. Furthermore, stronger reasoning capabilities in LLMs amplify these benefits. Our code is available at https://anonymous.4open.science/r/Cross-lingual-Collaboration."
}