| Challenge: | Existing neural models that use hand-crafted features are expensive and lack domain-specific knowledge. |
| Approach: | They propose a GEO model that uses operator-based features to generate equations using natural language sentences. |
| Outcome: | The proposed model outperforms state-of-the-art models on two datasets and 82.1% in ALG514. |
Similar Papers
Solving Math Word Problems with Multi-Encoders and Multi-Decoders (2020.coling-main)
Copied to clipboard
| Challenge: | Existing models that transform text descriptions into equation expressions only consider input/output objects as sequences, ignoring important structural information contained in text descriptions and equation expression. |
| Approach: | They propose a model that uses sequence-based encoders and graph-based decoders to enhance the representation of text descriptions and generate different equation expressions. |
| Outcome: | The proposed model outperforms existing state-of-the-art methods on a dataset with a n-word problem. |
Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems (N19-1)
Copied to clipboard
| Challenge: | Existing methods to solve math word problems require accurate natural language understanding to bridge texts and math expressions. |
| Approach: | They propose a neural approach to automatically solve math word problems by operating symbols according to their semantic meanings in texts. |
| Outcome: | The proposed model outperforms state-of-the-art models and the best non-retrieval-based models over 10% accuracy in a Math23K dataset. |
Tree-structured Decoding for Solving Math Word Problems (D19-1)
Copied to clipboard
| Challenge: | Existing approaches to solve math word problems do not consider an abstract syntax tree. |
| Approach: | They propose a tree-structured decoding method that generates an abstract syntax tree of an equation in a top-down manner and can stop during decoding without a redundant stop token. |
| Outcome: | The proposed method achieves state-of-the-art performance on the largest dataset on this task. |
Point to the Expression: Solving Algebraic Word Problems using the Expression-Pointer Transformer Model (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models that generate solution equations using ‘Op (operator/operand) tokens suffered expression fragmentation and operand-context separation. |
| Approach: | They propose a pure neural model, Expression-Pointer Transformer, which uses (1) ‘Expression’ token and (2) operand-context pointers when generating solution equations. |
| Outcome: | The proposed model achieves comparable performance accuracy to state-of-the-art models and achieves better performance than existing models by at most 40%. |
Seeking Diverse Reasoning Logic: Controlled Equation Expression Generation for Solving Math Word Problems (2022.aacl-short)
Copied to clipboard
| Challenge: | Existing methods to solve Math Word Problems rely on human annotation . empirical results suggest that our method universally improves the performance on single-unknown and multiple-un unknown benchmarks. |
| Approach: | They propose a controlled equation generation solver by leveraging a set of control codes to guide the model to consider certain reasoning logic and decode the corresponding equations expressions transformed from the human reference. |
| Outcome: | The proposed method improves performance on single-unknown and multiple-un unknown benchmarks with 13.2% accuracy on the challenging multiple-unequal datasets. |
An Expression Tree Decoding Strategy for Mathematical Equation Generation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to generate mathematical equations from natural language ignore parallel or dependent relations between math expressions. |
| Approach: | They propose to integrate tree structure into the expression-level generation and advocate an expression tree decoding strategy. |
| Outcome: | The proposed method outperforms baseline methods for generating mathematical equations from natural language. |
Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for generating mathematical word problems are lacking in educational assessment. |
| Approach: | They propose an end-to-end neural model to generate diverse mathematical word problems from commonsense knowledge graph and equations. |
| Outcome: | The proposed model outperforms the SOTA models in terms of evaluation metrics and topic relevance. |
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. |
GeoCoder: Solving Geometry Problems by Generating Modular Code through Vision-Language Models (2025.findings-naacl)
Copied to clipboard
| Challenge: | Various vision-language models (VLMs) have made significant progress in multimodal tasks, but they still struggle with geometry problems. |
| Approach: | They propose a vision-language model that leverages modular code-finetuning to generate and execute code using a predefined geometry function library. |
| Outcome: | The proposed model improves geometric reasoning abilities by 16% on a GeomVerse dataset compared to other methods. |
Generate & Rank: A Multi-task Framework for Math Word Problems (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies formalize MWP as a generation task but mathematical expressions are prone to minor mistakes. |
| Approach: | They propose a ranking task for math word problem (MWP) that learns from its own mistakes and distinguishes between correct and incorrect expressions. |
| Outcome: | The proposed model outperforms baselines on the classical Math23k dataset and is 7% higher than the state-of-the-art. |