无目标
发表于 2025-3-27 00:37:21
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Suggestions
发表于 2025-3-27 04:00:10
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壮观的游行
发表于 2025-3-27 08:19:16
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神秘
发表于 2025-3-27 10:18:06
From Plots to Endings: A Reinforced Pointer Generator for Story Ending Generatione a framework consisting of a Generator and a Reward Manager for this task. The Generator follows the pointer-generator network with coverage mechanism to deal with out-of-vocabulary (OOV) and repetitive words. Moreover, a mixed loss method is introduced to enable the Generator to produce story endi
Forage饲料
发表于 2025-3-27 17:12:39
A3Net:Adversarial-and-Attention Network for Machine Reading Comprehensionwo perspectives. First, adversarial training is applied to several target variables within the model, rather than only to the inputs or embeddings. We control the norm of adversarial perturbations according to the norm of original target variables, so that we can jointly add perturbations to several
天真
发表于 2025-3-27 21:21:02
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废止
发表于 2025-3-27 22:17:58
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弯腰
发表于 2025-3-28 03:37:06
Learning to Converse Emotionally Like Humans: A Conditional Variational Approachnt research hotspot. Although several emotional conversation approaches have been introduced, none of these methods were able to decide an appropriate emotion category for the response. We propose a new neural conversation model which is able to produce reasonable emotion interaction and generate em
Devastate
发表于 2025-3-28 06:49:17
Response Selection of Multi-turn Conversation with Deep Neural Networkss, the task is to choose the most reasonable response for the context. It can be regarded as a matching problem. To address this task, we propose a deep neural model named RCMN which focus on modeling relevance consistency of conversations. In addition, we adopt one existing deep learning model whic
Fillet,Filet
发表于 2025-3-28 10:54:40
Learning Dialogue History for Spoken Language Understandingesentations. SLU usually consists of two parts, namely intent identification and slot filling. Although many methods have been proposed for SLU, these methods generally process each utterance individually, which loses context information in dialogues. In this paper, we propose a hierarchical LSTM ba