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Found 5 papers, 3 papers with code
Point to the Expression: Solving Algebraic Word Problems using the Expression-Pointer Transformer Model
The contribution of this paper is two-fold; (1) We propose a pure neural model, EPT, which can address the expression fragmentation and the operand-context separation.
Ranked #3 on
Math Word Problem Solving
on DRAW-1K
How to Use Dropout Correctly on Residual Networks with Batch Normalization
In this study, we investigate the correct position to apply dropout.
GRPE: Relative Positional Encoding for Graph Transformer
The former loses preciseness of relative position from linearization, while the latter loses a tight integration of node-edge and node-topology interaction.
Ranked #13 on
Graph Regression
on PCQM4Mv2-LSC
Mounting Video Metadata on Transformer-based Language Model for Open-ended Video Question Answering
In this paper, we challenge the existing multiple-choice video question answering by changing it to open-ended video question answering.
Generating Equation by Utilizing Operators : GEO model
In this paper, we propose the GEO (Generation of Equations by utilizing Operators) model that does not use hand-crafted features and addresses two issues that are present in existing neural models: 1. missing domain-specific knowledge features and 2. losing encoder-level knowledge.
Ranked #2 on
Math Word Problem Solving
on DRAW-1K
