forthy
发表于 2025-3-23 12:17:29
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diabetes
发表于 2025-3-23 16:17:20
BB-KBQA: BERT-Based Knowledge Base Question Answeringuistic knowledge to obtain deep contextualized representations. Experimental results demonstrate that our model can achieve the state-of-the-art performance on the NLPCC- ICCPOL 2016 KBQA dataset, with an 84.12% averaged F1 score(1.65% absolute improvement).
擦掉
发表于 2025-3-23 19:35:42
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EXTOL
发表于 2025-3-23 22:21:10
Lecture Notes in Computer Scienced to explain the recognition ability of four NN-based models at a fine-grained level. The experimental results show that all the models have poor performance in the commonsense reasoning than in other entailment categories. The highest accuracy difference is 13.22%.
移动
发表于 2025-3-24 02:36:38
Paulin Jacobé de Naurois,Virgile Mogbilnd the interactive effects of keypoints in two sentences to learn sentence similarity. With less computational complexity, our model yields state-of-the-art improvement compared with other baseline models in paraphrase identification task on the Ant Financial competition dataset.
nonplus
发表于 2025-3-24 10:05:38
Synthesis Problems for One-Counter Automata,l results show due to different linguistic features, the neural components have different effects in English and Chinese. Besides, our models achieve state-of-the-art performance on CoNLL-2016 English and Chinese datasets.
有组织
发表于 2025-3-24 12:02:25
https://doi.org/10.1007/978-3-319-45994-3elected sentence by an abstractive decoder. Moreover, we apply the BERT pre-trained model as document encoder, sharing the context representations to both decoders. Experiments on the CNN/DailyMail dataset show that the proposed framework outperforms both state-of-the-art extractive and abstractive models.
Original
发表于 2025-3-24 18:30:29
Testing the Reasoning Power for NLI Models with Annotated Multi-perspective Entailment Datasetd to explain the recognition ability of four NN-based models at a fine-grained level. The experimental results show that all the models have poor performance in the commonsense reasoning than in other entailment categories. The highest accuracy difference is 13.22%.
Enteropathic
发表于 2025-3-24 22:59:04
ERCNN: Enhanced Recurrent Convolutional Neural Networks for Learning Sentence Similaritynd the interactive effects of keypoints in two sentences to learn sentence similarity. With less computational complexity, our model yields state-of-the-art improvement compared with other baseline models in paraphrase identification task on the Ant Financial competition dataset.
Absenteeism
发表于 2025-3-25 02:18:06
Comparative Investigation of Deep Learning Components for End-to-end Implicit Discourse Relationshipl results show due to different linguistic features, the neural components have different effects in English and Chinese. Besides, our models achieve state-of-the-art performance on CoNLL-2016 English and Chinese datasets.