官僚统治
发表于 2025-3-25 04:21:46
https://doi.org/10.1007/978-3-030-32233-5artificial intelligence; classification; computational linguistics; data mining; databases; fuzzy systems
DAMP
发表于 2025-3-25 10:06:05
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NIL
发表于 2025-3-25 13:18:15
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Consensus
发表于 2025-3-25 15:58:07
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远足
发表于 2025-3-25 21:00:14
Improving Question Answering by Commonsense-Based Pre-trainingsense knowledge. We believe the main reason is the lack of commonsense connections between concepts. To remedy this, we provide a simple and effective method that leverages external commonsense knowledge base such as ConceptNet. We pre-train direct and indirect relational functions between concepts,
dominant
发表于 2025-3-26 01:13:53
Multi-strategies Method for Cold-Start Stage Question Matching of rQA Tasksource limitation of many real applications, even the best SSEI models may underperform. To enhance the performance, this paper firstly proposes a novel deep neural network named Densely-connected Fusion Attentive Network (DFAN). The key idea behind our model is to learn the interactive semantic inf
贞洁
发表于 2025-3-26 04:30:19
Multilingual Dialogue Generation with Shared-Private Memorymultilingual dialogue system. Specifically, we augment the sequence to sequence framework with improved shared-private memory. The shared memory learns common features among different languages and facilitates a cross-lingual transfer to boost dialogue systems, while the private memory is owned by e
运动的我
发表于 2025-3-26 10:18:29
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审问
发表于 2025-3-26 12:53:01
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Fraudulent
发表于 2025-3-26 20:36:26
How Question Generation Can Help Question Answering over Knowledge Baseer. The task of question generation (QG) is to generate a corresponding natural language question given the input answer, while question answering (QA) is a reverse task to find a proper answer given the question. For the KBQA task, the answer could be regarded as a fact containing a predicate and t