A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for solving math word problems ignore background common-sense knowledge . a novel knowledge-aware sequence-to-tree (KA-S2T) network incorporates external knowledge and global expression information. |
| Approach: | They propose a knowledge-aware sequence-to-tree network that incorporates external knowledge and global expression information into the problem. |
| Outcome: | The proposed model can achieve better performance than previous models on a Math23K dataset. |
Similar Papers
Sequence to General Tree: Knowledge-Guided Geometry Word Problem Solving (2021.acl-short)
Copied to clipboard
| Challenge: | Existing neural solvers only generate binary expression trees that contain basic arithmetic operators and do not explicitly use the math formulas. |
| Approach: | They propose a sequence-to-general tree that generates interpretable and executable operation trees where nodes can be formulas with an arbitrary number of arguments. |
| Outcome: | The proposed tree generates interpretable and executable operation trees with formulas with an arbitrary number of arguments. |
An Edge-Enhanced Hierarchical Graph-to-Tree Network for Math Word Problem Solving (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work on graph neural networks to capture word relationships neglects the rest of the problem. |
| Approach: | They propose an edge-enhanced hierarchical graph encoder to incorporate edge label information. |
| Outcome: | The proposed model can improve performance on the MAWPS and Math23K datasets compared with state-of-the-art methods. |
Math Word Problem Solving with Explicit Numerical Values (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for solving math word problems ignore numerical values in solving problems. |
| Approach: | They propose a numerically-based approach that explicitly incorporates numerical values into a sequence-to-tree network and uses a mathematical properties prediction mechanism to capture category and comparison information of numerals. |
| Outcome: | The proposed model outperforms existing state-of-the-art models on the Math23K and APE datasets. |
Graph-to-Tree Learning for Solving Math Word Problems (2020.acl-main)
Copied to clipboard
| Challenge: | Existing tree-based neural models do not capture the relationships and order information among the quantities well. |
| Approach: | They propose a novel deep learning architecture that combines the merits of the graph-based encoder and tree-based decoder to generate better solution expressions. |
| Outcome: | The proposed framework outperforms the state-of-the-art on two available datasets significantly. |
Solving Math Word Problems with Multi-Encoders and Multi-Decoders (2020.coling-main)
Copied to clipboard
| Challenge: | Existing models that transform text descriptions into equation expressions only consider input/output objects as sequences, ignoring important structural information contained in text descriptions and equation expression. |
| Approach: | They propose a model that uses sequence-based encoders and graph-based decoders to enhance the representation of text descriptions and generate different equation expressions. |
| Outcome: | The proposed model outperforms existing state-of-the-art methods on a dataset with a n-word problem. |
Tree-KG: An Expandable Knowledge Graph Construction Framework for Knowledge-intensive Domains (2025.acl-long)
Copied to clipboard
| Challenge: | Knowledge graphs are a useful tool for organizing complex data in knowledge-intensive domains. |
| Approach: | They propose an expandable framework that combines structured domain texts with advanced semantic techniques to create a tree-like graph from textbooks. |
| Outcome: | The proposed framework surpasses competing methods in the text-Annotated dataset with high scores on the Text-Annalytated data. |
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Graph2Tree model encodes graph-structured input and decodes tree-structures output. |
| Approach: | They propose a novel Graph-to-Tree Neural Network consisting of a graph encoder and a hierarchical tree decoder that encodes an augmented graph-structured input and decodes a tree-structure-output. |
| Outcome: | The proposed model outperforms or matches the performance of other state-of-the-art models on two problems, neural semantic parsing and math word problem. |
Tree-structured Decoding for Solving Math Word Problems (D19-1)
Copied to clipboard
| Challenge: | Existing approaches to solve math word problems do not consider an abstract syntax tree. |
| Approach: | They propose a tree-structured decoding method that generates an abstract syntax tree of an equation in a top-down manner and can stop during decoding without a redundant stop token. |
| Outcome: | The proposed method achieves state-of-the-art performance on the largest dataset on this task. |
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning (D19-1)
Copied to clipboard
| Challenge: | empowering machines with the ability to perform commonsense reasoning has been seen as the bottleneck of artificial general intelligence . |
| Approach: | They propose a textual inference framework that uses external commonsense knowledge graphs to answer commonsensical questions. |
| Outcome: | The proposed framework is based on graph convolutional networks and LSTMs with a hierarchical path-based attention mechanism. |
Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models (N19-1)
Copied to clipboard
| Challenge: | a novel method for mapping unrestricted text to knowledge graph entities is proposed . a proof-of-concept experiment has encouraging results comparable to those of state-of the-art systems. |
| Approach: | They propose a method for mapping unrestricted text to knowledge graph entities by framing the task as a sequence-to-sequence problem. |
| Outcome: | The proposed method produces highly interpretable predictions comparable to state-of-the-art systems. |