![]() ![]() In addition, during generation, they focus on local features while neglecting global information. ![]() Previous methods have achieved promising results but ignore background common-sense knowledge not directly provided by the problem. Publisher = "Association for Computational Linguistics",Ībstract = "With the advancements in natural language processing tasks, math word problem solving has received increasing attention. Cite (Informal): A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving (Wu et al., EMNLP 2020) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: Video: = "A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving",īooktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", Association for Computational Linguistics. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7137–7146, Online. A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving. Anthology ID: 2020.emnlp-main.579 Volume: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) Month: November Year: 2020 Address: Online Venue: EMNLP SIG: Publisher: Association for Computational Linguistics Note: Pages: 7137–7146 Language: URL: DOI: 10.18653/v1/2020.emnlp-main.579 Bibkey: wu-etal-2020-knowledge Cite (ACL): Qinzhuo Wu, Qi Zhang, Jinlan Fu, and Xuanjing Huang. Experimental results on the Math23K dataset revealed that the KA-S2T model can achieve better performance than previously reported best results. Further, we use a tree-structured decoder with a state aggregation mechanism to capture the long-distance dependency and global expression information. Based on this entity graph, a graph attention network is used to capture knowledge-aware problem representations. To incorporate external knowledge and global expression information, we propose a novel knowledge-aware sequence-to-tree (KA-S2T) network in which the entities in the problem sequences and their categories are modeled as an entity graph. ![]() Abstract With the advancements in natural language processing tasks, math word problem solving has received increasing attention. ![]()
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