Graph-to-Graph: Towards Accurate and Interpretable Online Handwritten Mathematical Expression Recognition
Authors: Jin-Wen Wu, Fei Yin, Yan-Ming Zhang, Xu-Yao Zhang, Cheng-Lin Liu2925-2933
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on CROHME datasets to demonstrate the benefits of the proposed G2G model. Our method yields significant improvements over previous SOTA image-to-markup systems. |
| Researcher Affiliation | Academia | Jin-Wen Wu1,2 , Fei Yin1, Yan-Ming Zhang1, Xu-Yao Zhang1,2, Cheng-Lin Liu1,2,3 1 National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences 2 School of Artificial Intelligence, University of Chinese Academy of Sciences 3 CAS Center for Excellence of Brain Science and Intelligence Technology {jinwen.wu, fyin, ymzhang, xyz, liucl}@nlpr.ia.ac.cn |
| Pseudocode | No | The paper describes methods and models but does not contain a dedicated pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide any explicit statement or link to open-source code for the methodology described. |
| Open Datasets | Yes | We evaluate our model on the large public dataset available from the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) (Mouchère et al. 2016). |
| Dataset Splits | Yes | The CROHME training set contains 8,835 formulas with both symbol-level and expression-level annotations, and the test sets for CROHME 2013/2014/2016 contain 671/986/1,147 formulas, respectively. Consistent with participating systems in CROHME, we use the test set of CROHME 2013 as a validation set in training stage, and use the test sets of CROHME 2014 and 2016 to evaluate our proposed model. |
| Hardware Specification | Yes | Our models were implemented in Py Torch and optimized on two 12GB Nvidia TITAN X GPUs. |
| Software Dependencies | No | The paper mentions implementation in PyTorch but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The coefficients of different supervision losses are set experimentally. Specifically, we set λ1 = λ2 = λ6 = 0.5 to impose the same supervision on learning the representations of the nodes, edges and sub-graphs in the input graph. The supervision loss coefficients for the generation of nodes and edges in target graph are set to λ2 = λ3 = 1. We set λ5 = 0.3 to guide the distribution of attention coefficients on the source sub-graphs. The proposed model are optimized via the adaptive moment estimation (Adam, Kingma and Ba 2015) with learning rate 5e-4. Both the decoder and the encoder stack 3 GNN blocks. The network for pre-extracting the input primitive features has 4 blocks. We use 256 for the embedding dimension C of decoder GNN, and 400 for the dimension C of sub-graph attention. |