@inproceedings{pal-das-2025-toward,
    title = "Toward Quantum-Enhanced Natural Language Understanding: Sarcasm and Claim Detection with {QLSTM}",
    author = "Pal, Pritam  and
      Das, Dipankar",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.97/",
    pages = "854--859",
    abstract = "Traditional machine learning (ML) and deep learning (DL) models have shown effectiveness in natural language processing (NLP) tasks, such as sentiment analysis. However, they often struggle with complex linguistic structures, such as sarcasm and implicit claims. This paper introduces a Quantum Long Short-Term Memory (QLSTM) framework for detecting sarcasm and identifying claims in text, aiming to enhance the analysis of complex sentences. We evaluate four approaches: (1) classical LSTM, (2) quantum framework using QLSTM, (3) voting ensemble combining classical and quantum LSTMs, and (4) hybrid framework integrating both types. The experimental results indicate that the QLSTM approach excels in sarcasm detection, while the voting framework performs best in claim identification."
}