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苏劲松
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教授
- 入职时间：2011-07-25
- 所在单位：信息学院
- 学历：博士研究生毕业
- 性别：男
- 学位：工学博士学位
- 在职信息：在职
访问量：
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[221]苏劲松.Deconvolution-based global decoding for neural machine translation.COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings,3260-3271.
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[222]苏劲松.BACO: A background knowledge- And content-based framework for citing sentence generation.ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference,1466-1478.
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[223]苏劲松.Exploring dynamic selection of branch expansion orders for code generation.ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference,5076-5085.
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[224]苏劲松.Towards user-driven neural machine translation.ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference,4008-4018.
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[225]苏劲松.Bridging subword gaps in pretrain-finetune paradigm for natural language generation.ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference,6001-6011.
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[226]苏劲松.A novel graph-based multi-modal fusion encoder for neural machine translation.Proceedings of the Annual Meeting of the Association for Computational Linguistics,3025-3035.
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[227]苏劲松.Modeling discourse structure for document-level neural machine translation.Proceedings of the Annual Meeting of the Association for Computational Linguistics,30-36.
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[228]苏劲松.Exploring contextual word-level style relevance for unsupervised style transfer.Proceedings of the Annual Meeting of the Association for Computational Linguistics,7135-7144.
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[229]苏劲松.A Real-Time Global Inference Network for One-Stage Referring Expression Comprehension.IEEE Transactions on Neural Networks and Learning Systems,
