| Challenge: | Existing studies have shown the effectiveness of sequence-to-sequence (Seq2Seque) on mathematics solving. |
| Approach: | They propose a graph-to-sequence neural network which can learn hierarchical information of graphs inputs to solve mathematical problems and speculate answers. |
| Outcome: | The proposed neural network outperforms other neural networks in hidden information learning and mathematics resolving. |
Similar Papers
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. |
A Survey of Deep Learning for Mathematical Reasoning (2023.acl-long)
Copied to clipboard
| Challenge: | a survey of deep learning for mathematical reasoning examines the field . a comprehensive reading list is provided to assist readers interested in the field. |
| Approach: | They present a survey of deep learning for mathematical reasoning over the past decade . they outline directions for future research and highlight potential for further exploration . |
| Outcome: | The proposed framework is based on the results of a decade-long survey of deep learning for mathematical reasoning. |
Graph-to-Sequence Learning using Gated Graph Neural Networks (P18-1)
Copied to clipboard
| Challenge: | Existing approaches to graph-to-sequence learning ignore the full graph structure, discarding key information. |
| Approach: | They propose a graph-to-sequence learning model that encodes the full graph structure and an input transformation that allows nodes and edges to have their own hidden representations. |
| Outcome: | The proposed model outperforms baselines in generation from AMR graphs and syntax-based neural machine translation while retaining the full graph structure. |
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing question answering systems lack the ability to access relevant knowledge and reason over it. |
| Approach: | They propose a model that uses KGs to identify relevant knowledge in QA contexts and perform joint reasoning over them. |
| Outcome: | The proposed model improves on the CommonsenseQA and OpenBookQA datasets and performs interpretable and structured reasoning. |
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. |
ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering over Knowledge Graph (2023.emnlp-main)
Copied to clipboard
| Challenge: | Question Answering over Knowledge Graph (KGQA) aims to find answer entities for natural language questions based on knowledge graphs. |
| Approach: | They propose a subgraph-aware self-attention mechanism to imitate the graph neural network (GNN) based module to perform multi-hop reasoning on KG. |
| Outcome: | The proposed method surpasses state-of-the-art models by a large margin even with fewer updated parameters and less training data. |
Question Answering by Reasoning Across Documents with Graph Convolutional Networks (N19-1)
Copied to clipboard
| Challenge: | Recent research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. |
| Approach: | They propose a neural model which integrates and reasons relying on information spread within documents and across multiple documents. |
| Outcome: | The proposed model achieves state-of-the-art on a multi-document question answering dataset, WikiHop. |
MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms (N19-1)
Copied to clipboard
| Challenge: | Existing datasets in this domain do not offer precise operational annotations over diverse problem types due to noise and lack of formal operation-based representations. |
| Approach: | They propose a representation language to map problems to their operation programs . they also introduce an interpretable neural math problem solver . |
| Outcome: | The proposed model outperforms baseline models and the AQUA-RAT dataset on the AQuA-rat dataset. |
Neural Machine Translation for Mathematical Formulae (2023.acl-long)
Copied to clipboard
| Challenge: | a recent paper examines the problem of neural machine translation of mathematical formulae between ambiguous presentation languages and unambiguous content languages. |
| Approach: | They perform translation tasks from LaTeX to Mathematica and from La TeX into semantic LaTaX using convolutional sequence-to-sequence networks. |
| Outcome: | The proposed translations achieve 95.1% and 90.7% exact matches between the two languages. |
HyperKGR: Knowledge Graph Reasoning in Hyperbolic Space with Graph Neural Network Encoding Symbolic Path (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for linking knowledge graphs are incomplete and rely on Euclidean embeddings . a hyperbolic GNN framework embeds recursive learning trees in hyperbolical space . |
| Approach: | They propose a hyperbolic GNN framework that embeds recursive learning trees in hyperbolical space and generates query-specific embeddings. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on multiple benchmark datasets. |