標(biāo)題: Titlebook: Deep Learning Approaches to Text Production; Shashi Narayan,Claire Gardent Book 2020 Springer Nature Switzerland AG 2020 [打印本頁] 作者: 助手 時間: 2025-3-21 16:22
書目名稱Deep Learning Approaches to Text Production影響因子(影響力)
書目名稱Deep Learning Approaches to Text Production影響因子(影響力)學(xué)科排名
書目名稱Deep Learning Approaches to Text Production網(wǎng)絡(luò)公開度
書目名稱Deep Learning Approaches to Text Production網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Learning Approaches to Text Production被引頻次
書目名稱Deep Learning Approaches to Text Production被引頻次學(xué)科排名
書目名稱Deep Learning Approaches to Text Production年度引用
書目名稱Deep Learning Approaches to Text Production年度引用學(xué)科排名
書目名稱Deep Learning Approaches to Text Production讀者反饋
書目名稱Deep Learning Approaches to Text Production讀者反饋學(xué)科排名
作者: 暴發(fā)戶 時間: 2025-3-21 22:02 作者: overture 時間: 2025-3-22 03:43 作者: 憂傷 時間: 2025-3-22 05:21 作者: immunity 時間: 2025-3-22 09:34 作者: Increment 時間: 2025-3-22 14:53 作者: Increment 時間: 2025-3-22 18:06
Modelling Task-Specific Communication Goals, we will discuss how communication goal-oriented generators can be useful for text production. In particular, we will focus on generators that are specifically trained for summarisation, simplification, to profile user for dialogue-response generation, or to generate from loosely aligned data.作者: FOVEA 時間: 2025-3-22 23:56
Book 2020ases, or generate English sentences from rich linguistic representations, such as dependency trees or abstract meaning representations. Text production is also at work in text-to-text transformations such as sentence compression, sentence fusion, paraphrasing, sentence (or text) simplification, and 作者: 生氣的邊緣 時間: 2025-3-23 02:24
1947-4040 ising a text, or how to produce a well-formed text that correctly captures the information contained in some input data in the case of data-to-text generation).978-3-031-01045-3978-3-031-02173-2Series ISSN 1947-4040 Series E-ISSN 1947-4059 作者: 誹謗 時間: 2025-3-23 09:23 作者: 圓錐 時間: 2025-3-23 13:08
Design and Use of Assistive Technologyned with (i.e., text production from data, from text, and from meaning representations) and we summarise the content of each chapter. We also indicate what is not covered and introduce some notational conventions.作者: confederacy 時間: 2025-3-23 14:26 作者: Cardioplegia 時間: 2025-3-23 20:36
https://doi.org/10.1007/978-3-540-74111-4pipeline of modules, each performing a specific subtask. The neural approach is very different from the pre-neural approach in that it provides a uniform (end-to-end) framework for text production. First the input is projected on a continuous representation (representation learning), and then, the g作者: figurine 時間: 2025-3-23 23:35 作者: motor-unit 時間: 2025-3-24 06:20 作者: 木訥 時間: 2025-3-24 09:00 作者: Armory 時間: 2025-3-24 13:23
Synthesis Lectures on Human Language Technologieshttp://image.papertrans.cn/d/image/264571.jpg作者: 值得贊賞 時間: 2025-3-24 15:57 作者: Immunization 時間: 2025-3-24 19:52
Pre-Neural Approachesltiple, interacting factors and differ depending on the NLG task they address. More specifically, three main types of pre-neural NLG architectures can be distinguished depending on whether the task is to generate from data from meaning representations or text.作者: 巨碩 時間: 2025-3-24 23:19
Generating Better Textome data), is first encoded into a continuous representation. This representation is then input to the decoder, which predicts output words, one step at a time, conditioned both on the input representation and on the previously predicted words.作者: 收藏品 時間: 2025-3-25 04:29 作者: 人類 時間: 2025-3-25 07:54 作者: 大都市 時間: 2025-3-25 15:14 作者: indoctrinate 時間: 2025-3-25 17:16 作者: Femish 時間: 2025-3-25 20:31 作者: 土產(chǎn) 時間: 2025-3-26 03:52
Thomas English,Garrison W. Greenwoodltiple, interacting factors and differ depending on the NLG task they address. More specifically, three main types of pre-neural NLG architectures can be distinguished depending on whether the task is to generate from data from meaning representations or text.作者: Phonophobia 時間: 2025-3-26 07:00
Natural Processes and Artificial Proceduresome data), is first encoded into a continuous representation. This representation is then input to the decoder, which predicts output words, one step at a time, conditioned both on the input representation and on the previously predicted words.作者: Schlemms-Canal 時間: 2025-3-26 09:18
Introduction,ned with (i.e., text production from data, from text, and from meaning representations) and we summarise the content of each chapter. We also indicate what is not covered and introduce some notational conventions.作者: 品牌 時間: 2025-3-26 14:48
Pre-Neural Approachesltiple, interacting factors and differ depending on the NLG task they address. More specifically, three main types of pre-neural NLG architectures can be distinguished depending on whether the task is to generate from data from meaning representations or text.作者: Heresy 時間: 2025-3-26 17:48 作者: Daily-Value 時間: 2025-3-26 22:23 作者: Ingrained 時間: 2025-3-27 03:57 作者: airborne 時間: 2025-3-27 06:19 作者: 炸壞 時間: 2025-3-27 11:33 作者: Inelasticity 時間: 2025-3-27 17:19 作者: 故意釣到白楊 時間: 2025-3-27 18:12
infectious diseases, allergy, and hypersensitivity. It is anticipated that by presenting information concerning these apparently heterogeneous topics under the unifying umbrella of the RES attention will be focused on the similarities as well as interactions among the cell types constitut- ing the RES from t978-1-4684-4168-0978-1-4684-4166-6作者: 壯麗的去 時間: 2025-3-28 01:33 作者: 花費 時間: 2025-3-28 05:27
R. Slanskyadult with or at risk of fragility fracture. It considers this from the perspectives of all of the settings in which this group of patients receive nursing care.?.Globally, a fragility fracture is estimated to occur every 3 seconds. This amounts to 25 000 fractures per day or 9 million per year. The作者: cravat 時間: 2025-3-28 08:47
