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. |
Similar Papers
Tree-Based Representation and Generation of Natural and Mathematical Language (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models for generating and modeling mathematical language are limited . existing models for modeling and generating mathematical language simply treat mathematical expressions as text . |
| Approach: | They propose to combine mathematical expressions and text-based models to generate mathematically valid expressions. |
| Outcome: | The proposed model outperforms baselines on mathematical expression generation tasks. |
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. |
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. |
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. |
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. |
Translating a Math Word Problem to a Expression Tree (D18-1)
Copied to clipboard
| Challenge: | Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. |
| Approach: | They propose an equation normalization method to normalize duplicated equations and propose an ensemble model to combine their advantages. |
| Outcome: | The proposed model outperforms the previous state-of-the-art models on the math word problem solving. |
Text2Math: End-to-end Parsing Text into Math Expressions (D19-1)
Copied to clipboard
| Challenge: | Empirical results on benchmark datasets demonstrate the efficacy of our approach. |
| Approach: | They propose a model for semantically parsing text into math expressions and propose 'text2math' which aims to predict the complete math expression as a tree structure, with minimal manual efforts. |
| Outcome: | Empirical results on benchmark datasets demonstrate the efficacy of the proposed model. |
Generating Equation by Utilizing Operators : GEO model (2020.coling-main)
Copied to clipboard
| 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. |
Graph-to-Tree Learning for Solving Math Word Problems (2020.acl-main)
Copied to clipboard
| Challenge: | Existing tree-based neural models do not capture the relationships and order information among the quantities well. |
| Approach: | They propose a novel deep learning architecture that combines the merits of the graph-based encoder and tree-based decoder to generate better solution expressions. |
| Outcome: | The proposed framework outperforms the state-of-the-art on two available datasets significantly. |
Analysis of Tree-Structured Architectures for Code Generation (2021.findings-acl)
Copied to clipboard
| Challenge: | Code generation is the task of generating code snippets from input user specifications written in natural language (NL). |
| Approach: | They evaluate the significance of input parse trees for code generation by using constituency-based parsers as input and an abstract syntax tree as the target. |
| Outcome: | The proposed models on a Python-based code generation dataset and a semantic parsing dataset show that constituency trees encoded using a structure-aware model improve performance. |