Improving quality and domain-relevancy of paraphrase generation with graph-based retrieval augmented generation.
(2024)
Presentation / Conference Contribution
JAYAWARDENA, L. and YAPA, P. 2025. Improving quality and domain-relevancy of paraphrase generation with graph-based retrieval augmented generation. In Proceedings of the 10th International conference on computing and artificial intelligence 2024 (ICCAI'24)[online], pages 196-208. Available from: https://doi.org/10.1145/3669754.366978
Paraphrase generation is a fundamental area of research in Natural Language Processing (NLP) and Natural Language Generation (NLG), due to its sequence-to-sequence (Seq2Seq) nature. Paraphrasing, spanning across various domains, poses challenges for... Read More about Improving quality and domain-relevancy of paraphrase generation with graph-based retrieval augmented generation..