https://scholars.lib.ntu.edu.tw/handle/123456789/559425
標題: | Dual supervised learning for natural language understanding and generation | 作者: | Su, S.-Y. Huang, C.-W. YUN-NUNG CHEN |
公開日期: | 2020 | 起(迄)頁: | 5472-5477 | 來源出版物: | ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference | 摘要: | Natural language understanding (NLU) and natural language generation (NLG) are both critical research topics in the NLP and dialogue fields. Natural language understanding is to extract the core semantic meaning from the given utterances, while natural language generation is opposite, of which the goal is to construct corresponding sentences based on the given semantics. However, such dual relationship has not been investigated in literature. This paper proposes a novel learning framework for natural language understanding and generation on top of dual supervised learning, providing a way to exploit the duality. The preliminary experiments show that the proposed approach boosts the performance for both tasks, demonstrating the effectiveness of the dual relationship.1. © 2019 Association for Computational Linguistics |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/559425 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070070917&partnerID=40&md5=8d00cb15c4359082ff8c81e4379be402 |
SDG/關鍵字: | Computational linguistics; Semantics; Supervised learning; Critical researches; Learning frameworks; Natural language generation; Natural language understanding; Natural language processing systems |
顯示於: | 資訊工程學系 |
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