Robust Understanding of Word Problems with Extraneous Information

By Yefim Bakman. 2007

93 citations recorded by the source corpus.

Understanding of free-format multi-step arithmetic word problems with extraneous information is discussed. A model including a full set of general skills necessary for understanding such problems was developed and computer implemented. The validity of the simulation was confirmed by testing on a variety of word problems with extraneous information. The results of this study show the way for moving from an intuitive understanding of word problems to a conscious and controlled process.

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms (2019, background): “Many early works focused on solving addition and subtraction problems (Briars and Larkin, 1984; Dellarosa, 1986; Bakman, 2007).”
A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers (2020, background): “Among them, math word problems (MWPs) are frequently chosen to study natural language understanding and simulate human problem solving (Bakman, 2007; Mukherjee and Garain, 2008; Liang et al., 2016), because the answer is not a span within the given problem text that can be directly extracted.”
Deep Neural Solver for Math Word Problems (2017, background): “In addition, Addition/subtraction problems are studied most Bri-ars and Larkin (1984); Dellarosa (1986); Bakman (2007); Yuhui et al. (2010); Roy et al. (2015).”
How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation (2016, background): “Addition/subtraction problems are studied most in early research (Briars and Larkin, 1984; Fletcher, 1985; Dellarosa, 1986; Bakman, 2007; Yuhui et al., 2010).”
Automatically Solving Number Word Problems by Semantic Parsing and Reasoning (2015, background): “In symbolic approaches (Bobrow, 1964a, b; Charniak, 1968; Bakman, 2007; Liguda & Pfeiffer, 2012), math problem sentences are transformed to certain structures by pattern matching or verb categorization.”
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.”
Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks (2021, methodology): “Numerous methods have been proposed to tackle the MWP solving task, ranging from rule-based methods (Bakman, 2007; Yuhui et al., 2010), statistical machine learning methods (Kushman et al.”
Solving Math Word Problems with Teacher Supervision (2021, methodology): “MWPs were initially solved by rule-based methods [ Fletcher, 1985; Bakman, 2007 ] and traditional statistical methods [ Mitra and Baral, 2016; Kushman et al. , 2014 ] .”
Neural Math Word Problem Solver with Reinforcement Learning (2018, methodology): “Rule-based approaches (Bobrow, 1964a; Bobrow, 1964b; Bakman, 2007; Liguda and Pfeiffer, 2012; Shi et al., 2015) accept only well formed input sentences and map them into predefined structures by rules.”
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.”
Learning Fine-Grained Expressions to Solve Math Word Problems (2017, methodology): “Automatic math word problem solving methods (Bobrow, 1964a,b; Charniak, 1968, 1969; Briars and Larkin, 1984; Fletcher, 1985; Dellarosa, 1986; Bakman, 2007; Yuhui et al., 2010) developed before 2008 are mostly rule-based.”
Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems (2020, methodology): “Numerous methods have been proposed to attack the MWPs task, ranging from rule-based meth-ods (Bakman, 2007; Yuhui et al., 2010), statistical machine learning methods (Kushman et al., 2014; Zhou et al., 2015; Mitra and Baral, 2016; Huang et al., 2016; Roy and Roth, 2018),semantic parsing methods…”
A Meaning-Based Statistical English Math Word Problem Solver (2018, background): “The math word problem (MWP) (see Figure 1) is frequently chosen to study natural language understanding and simulate human problem solving (Bakman, 2007; Hosseini et al., 2014; Liang et al., 2016) for the following reasons: (1) the answer to the MWP cannot be simply extracted by performing keyword/p”
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.”
A review of methods for automatic understanding of natural language mathematical problems (2008, background): “The ROBUST simulation works by first parsing the problem text to split all sentences into propositions or simple sentences like ‘David gave 3 candies to Ruth’, ‘David has ? candies’ etc. for the above problem.”
Natural Language Processing for Solving Simple Word Problems (2015, methodology): “ROBUST, the system developed by (Bakman, 2007), was able to solve multi-step problems with extraneous information.”
A Bottom-Up DAG Structure Extraction Model for Math Word Problems (2021, methodology): “The technique behind the solver evolves from rule-based (Bakman 2007), statistical learning (Koncel-Kedziorski et al.”
Using Intermediate Representations to Solve Math Word Problems (2018, background): “Bakman (2007) defines a table of schema (e.”