Heterogeneous Line Graph Transformer for Math Word Problems

• URL: http://arxiv.org/abs/2208.05645v2
• Date: Fri, 12 Aug 2022 01:56:23 GMT
• Title: Heterogeneous Line Graph Transformer for Math Word Problems
• Authors: Zijian Hu and Meng Jiang
• Abstract summary: This paper describes the design and implementation of a new machine learning model for online learning systems. We aim at improving the intelligent level of the systems by enabling an automated math word problem solver.
• Score: 21.4761673982334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper describes the design and implementation of a new machine learning model for online learning systems. We aim at improving the intelligent level of the systems by enabling an automated math word problem solver which can support a wide range of functions such as homework correction, difficulty estimation, and priority recommendation. We originally planned to employ existing models but realized that they processed a math word problem as a sequence or a homogeneous graph of tokens. Relationships between the multiple types of tokens such as entity, unit, rate, and number were ignored. We decided to design and implement a novel model to use such relational data to bridge the information gap between human-readable language and machine-understandable logical form. We propose a heterogeneous line graph transformer (HLGT) model that constructs a heterogeneous line graph via semantic role labeling on math word problems and then perform node representation learning aware of edge types. We add numerical comparison as an auxiliary task to improve model training for real-world use. Experimental results show that the proposed model achieves a better performance than existing models and suggest that it is still far below human performance. Information utilization and knowledge discovery is continuously needed to improve the online learning systems.

Related papers

• Shedding Light on Problems with Hyperbolic Graph Learning [2.3743504594834635]
  Recent papers in the graph machine learning literature have introduced a number of approaches for hyperbolic representation learning. We take a careful look at the field of hyperbolic graph representation learning as it stands today. We find that a number of papers fail to diligently present baselines, make faulty modelling assumptions when constructing algorithms, and use misleading metrics to quantify geometry of graph datasets.
  arXiv  Detail & Related papers  (2024-11-11T03:12:41Z)
• Graph-Dictionary Signal Model for Sparse Representations of Multivariate Data [49.77103348208835]
  We define a novel Graph-Dictionary signal model, where a finite set of graphs characterizes relationships in data distribution through a weighted sum of their Laplacians. We propose a framework to infer the graph dictionary representation from observed data, along with a bilinear generalization of the primal-dual splitting algorithm to solve the learning problem. We exploit graph-dictionary representations in a motor imagery decoding task on brain activity data, where we classify imagined motion better than standard methods.
  arXiv  Detail & Related papers  (2024-11-08T17:40:43Z)
• NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification [70.51126383984555]
  We introduce a novel all-pair message passing scheme for efficiently propagating node signals between arbitrary nodes. The efficient computation is enabled by a kernerlized Gumbel-Softmax operator. Experiments demonstrate the promising efficacy of the method in various tasks including node classification on graphs.
  arXiv  Detail & Related papers  (2023-06-14T09:21:15Z)
• Automatic tagging of knowledge points for K12 math problems [3.703920945313331]
  There are few studies on the automatic tagging of knowledge points for math problems. Math texts have more complex structures and semantics compared with general texts. The model combines the text classification techniques in general domains and the unique features of math texts.
  arXiv  Detail & Related papers  (2022-08-21T11:11:30Z)
• A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations [39.8498896531672]
  Semi-supervised learning on graphs is a widely applicable problem in network science and machine learning. We develop a Markov random field model for the data generation process of node attributes. We show that label propagation, a linearized graph convolutional network, and their combination can all be derived as conditional expectations.
  arXiv  Detail & Related papers  (2021-01-19T17:07:08Z)
• SMART: A Situation Model for Algebra Story Problems via Attributed Grammar [74.1315776256292]
  We introduce the concept of a emphsituation model, which originates from psychology studies to represent the mental states of humans in problem-solving. We show that the proposed model outperforms all previous neural solvers by a large margin while preserving much better interpretability.
  arXiv  Detail & Related papers  (2020-12-27T21:03:40Z)
• Learning outside the Black-Box: The pursuit of interpretable models [78.32475359554395]
  This paper proposes an algorithm that produces a continuous global interpretation of any given continuous black-box function. Our interpretation represents a leap forward from the previous state of the art.
  arXiv  Detail & Related papers  (2020-11-17T12:39:44Z)
• Multilayer Clustered Graph Learning [66.94201299553336]
  We use contrastive loss as a data fidelity term, in order to properly aggregate the observed layers into a representative graph. Experiments show that our method leads to a clustered clusters w.r.t. We learn a clustering algorithm for solving clustering problems.
  arXiv  Detail & Related papers  (2020-10-29T09:58:02Z)
• Category-Learning with Context-Augmented Autoencoder [63.05016513788047]
  Finding an interpretable non-redundant representation of real-world data is one of the key problems in Machine Learning. We propose a novel method of using data augmentations when training autoencoders. We train a Variational Autoencoder in such a way, that it makes transformation outcome predictable by auxiliary network.
  arXiv  Detail & Related papers  (2020-10-10T14:04:44Z)
• Are Hyperbolic Representations in Graphs Created Equal? [1.80476943513092]
  We consider whether non-Euclidean embeddings are always useful for graph learning tasks. We first fix an issue of the existing models associated with the optimization process at zero curvature. We evaluate the approach of embedding graphs into the manifold in several graph representation learning tasks.
  arXiv  Detail & Related papers  (2020-07-15T14:14:14Z)
• Non-Parametric Graph Learning for Bayesian Graph Neural Networks [35.88239188555398]
  We propose a novel non-parametric graph model for constructing the posterior distribution of graph adjacency matrices. We demonstrate the advantages of this model in three different problem settings: node classification, link prediction and recommendation.
  arXiv  Detail & Related papers  (2020-06-23T21:10:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.