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Found 8 papers, 2 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
BPM_MT: Enhanced Backchannel Prediction Model using Multi-Task Learning
Backchannel (BC), a short reaction signal of a listener to a speaker’s utterances, helps to improve the quality of the conversation.
EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers
(1) EPT-X model: An explainable neural model that sets a baseline for algebraic word problem solving task, in terms of model’s correctness, plausibility, and faithfulness.
Ranked #1 on
Math Word Problem Solving
on PEN
Automatic Gloss-level Data Augmentation for Sign Language Translation
In this study, we present three methods of augmenting sign language text modality data, comprising 3, 052 Gloss-level Korean Sign Language (GKSL) and Word-level Korean Language (WKL) sentence pairs.
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
