Understanding and solving arithmetic word problems: A computer simulation

By C. Fletcher. 1985

Subfields: Cognitive Science & Psychology

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Citations in the corpus

Generate & Rank: A Multi-task Framework for Math Word Problems (2021, neutral): “Early approaches on math word problems mainly craft rules and templates for pattern matching (Bobrow, 1964; Slagle, 1965; Fletcher, 1985; Bakman, 2007).”
Deep Neural Solver for Math Word Problems (2017, background): “Developing computer models to automatically solve math word problems has been an interest of NLP researchers since 1963 Feigenbaum et al. (1963); Bobrow (1964); Briars and Larkin (1984); Fletcher (1985).”
How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation (2016, background): “Designing computer systems for automatically solving math word problems is a challenging research topic that dates back to the 1960s (Bobrow, 1964a; Briars and Larkin, 1984; Fletcher, 1985).”
Automatically Solving Number Word Problems by Semantic Parsing and Reasoning (2015, background): “Addition/subtraction problems are studied most in early research (Bri-ars & Larkin, 1984; Fletcher, 1985; Dellarosa, 1986; Bakman, 2007; Ma et al., 2010).”
A Goal-Driven Tree-Structured Neural Model for Math Word Problems (2019, methodology): “Research in automatically solving MWPs has a very long history, ranging from rule-based methods [Fletcher, 1985; Bakman, 2007; Yuhui et al., 2010], statistical machine learning methods [Kushman et al.”
Robust Understanding of Word Problems with Extraneous Information (2007, methodology): “Both models were computer-implemented (see Dellarosa, 1986; Fletcher, 1985; Briars and Larkin, 1984) and their ability to solve one-step problems with only one possible operation - addition/subtraction - was proven.”
Learning by Fixing: Solving Math Word Problems with Weak Supervision (2020, background): “Previous studies of MWPs range from traditional rule-based methods (Fletcher 1985; Bakman 2007; Yu-hui et al. 2010), statistical learning methods (Kushman et al. 2014; Zhou, Dai, and Chen 2015; Mitra and Baral 2016; Roy and Roth 2017; Huang et al. 2016), semantic-parsing meth-ods (Shi et al. 2015;…”
Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems (2021, unknown): “The methods can be divided into four categories: rule-based methods (Fletcher, 1985; Bakman, 2007), statistical machine learning methods (Kushman et al.”
Solving Math Word Problems with Multi-Encoders and Multi-Decoders (2020, unknown): “Early researches focused on rule-based methods (Fletcher, 1985; Bakman, 2007; Yuhui et al., 2010) and statistical machine learning methods (Kushman et al.”
A Weakly Supervised Model for Solving Math word Problems (2021, background): “Rule-based systems (Fletcher, 1985; Bakman, 2007; Yuhui et al., 2010) were amongst the earliest approaches to solve MWPs.”
The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers (2018, methodology): “In the ﬁrst pioneering stage, roughly from the year 1960 to 2010 , systems such as STUDENT [1], DEDUCOM [6], WORDPRO [7] and ROBUST [8], manually craft rules and schemas for pattern matchings.”
MWP-BERT: A Strong Baseline for Math Word Problems (2021, methodology): “MWP solving was presented as a task for artiﬁcial intelligence several decades ago (Fletcher, 1985), where earlier studies usually applied rule-based methods.”
The Effects of Mathematics Strategy Instruction for Children with Serious Problem-Solving Difficulties (2014, background): “The mechanisms that underlie such differentiation play a key role in mental problem representation (e.g., Kintsch & Greeno, 1985).”
HMS: A Hierarchical Solver with Dependency-Enhanced Understanding for Math Word Problem (2021, methodology): “Specifically, rule-based methods relied on manually crafted rules to match predeﬁned problem templates, such as (Fletcher 1985) and (Yuhui et al. 2010).”