Neural natural language generation

• Verfasst von: Erdem, Erkut [VerfasserIn]
• Verfasst von: Kuyu, Menekse [VerfasserIn]
• Verfasst von: Yagcioglu, Semih [VerfasserIn]
• Verfasst von: Frank, Anette [VerfasserIn]
• Verfasst von: Pârcălăbescu, Letiția [VerfasserIn]
• Verfasst von: Plank, Barbara [VerfasserIn]
• Verfasst von: Babii, Andrii [VerfasserIn]
• Verfasst von: Turuta, Oleksii [VerfasserIn]
• Verfasst von: Erdem, Aykut [VerfasserIn]
• Verfasst von: Calixto, Iacer [VerfasserIn]
• Verfasst von: Lloret, Elena [VerfasserIn]
• Verfasst von: Apostol, Elena-Simona [VerfasserIn]
• Verfasst von: Truică, Ciprian-Octavian [VerfasserIn]
• Verfasst von: Šandrih, Branislava [VerfasserIn]
• Verfasst von: Martinčić-Ipšić, Sanda [VerfasserIn]
• Verfasst von: Berend, Gábor [VerfasserIn]
• Verfasst von: Gatt, Albert [VerfasserIn]
• Verfasst von: Korvel, Grăzina [VerfasserIn]
• Titel: Neural natural language generation
• Titelzusatz: a survey on multilinguality, multimodality, controllability and learning
• Verf.angabe: Erkut Erdem, Menekse Kuyu, Semih Yagcioglu, Anette Frank, Letitia Parcalabescu, Barbara Plank, Andrii Babii, Oleksii Turuta, Aykut Erdem, Iacer Calixto, Elena Lloret, Elena-Simona Apostol, Ciprian-Octavian Truică, Branislava Šandrih, Sanda Martinčić-Ipšić, Gábor Berend, Albert Gatt, Grăzina Korvel
• E-Jahr: 2022
• Jahr: Apr 6, 2022
• Umfang: 77 S.
• Fussnoten: Gesehen am 05.10.2022
• Titel Quelle: Enthalten in: Journal of artificial intelligence research
• Ort Quelle: [S.l.] : AI Access Found., 1994
• Jahr Quelle: 2022
• Band/Heft Quelle: 73(2022), Seite 1131-1207
• ISSN Quelle: 1943-5037
• DOI: doi:10.1613/jair.1.12918
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: natural language
• Sach-SW: neural networks
• K10plus-PPN: 1818045001

Abstract

Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led to neural approaches to natural language generation (NLG). These methods combine generative language learning techniques with neural-networks based frameworks. With a wide range of applications in natural language processing, neural NLG (NNLG) is a new and fast growing field of research. In this state-of-the-art report, we investigate the recent developments and applications of NNLG in its full extent from a multidimensional view, covering critical perspectives such as multimodality, multilinguality, controllability and learning strategies. We summarize the fundamental building blocks of NNLG approaches from these aspects and provide detailed reviews of commonly used preprocessing steps and basic neural architectures. This report also focuses on the seminal applications of these NNLG models such as machine translation, description generation, automatic speech recognition, abstractive summarization, text simplification, question answering and generation, and dialogue generation. Finally, we conclude with a thorough discussion of the described frameworks by pointing out some open research directions.