Natural Language Input for a Computer Problem Solving System

By D. Bobrow. 1964

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In Natural Language Processing

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MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers (2021, background): “Developing automatic Math Word Problems (MWPs) solvers has been an interest of NLP researchers since the 1960s (Feigenbaum and Feldman, 1963; Bobrow, 1964).”
Measuring and Improving BERT’s Mathematical Abilities by Predicting the Order of Reasoning. (2021, background): “(Bobrow, 1964; Char-niak, 1969; Shi et al., 2015; Ling et al., 2017; Amini et al., 2019; Parisotto et al., 2016; Wang et al., 2018; Zou and Lu, 2019) as well as datasets (Ling et al., 2017; Amini et al., 2019; Huang et al., 2016; Saxton et al., 2019) An interesting approach is proposed in (Rabe et…”
MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms (2019, methodology): “Solving with Handcrafted Features Due to sparsity of suitable data, early work on math word problem solving used pattern-matching to map word problems to mathematical expressions (Bobrow, 1964; Charniak, 1968, 1969), as well as non-neural statistical modeling and semantic parsing approaches (Liguda ”
Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction (2022, background): “Math word problem (MWP) solving (Bobrow, 1964) is a task of answering a mathematical question that is described in natural language.”
Reasoning about Quantities in Natural Language (2015, methodology): “Approaches range from using rule-based methods (Bobrow, 1964; Lev et al., 2004; Mukherjee and Garain, 2008) to recent template matching techniques (Kushman et al.”
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).”
On the Binding Problem in Artificial Neural Networks (2020, background): “problems (Bobrow, 1964), or control simple virtual block worlds (Winograd, 1971).”
Automatically Solving Number Word Problems by Semantic Parsing and Reasoning (2015, background): “Efforts to automatically solve math word problems date back to the 1960s (Bobrow, 1964a, b).”
MathDQN: Solving Arithmetic Word Problems via Deep Reinforcement Learning (2018, background): “Automatically solving math word problems (MWP) has a long history dating back to 1960s (Bobrow 1964) and still continues to attract researchers’ attention in recent years.”
Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions (2019, background): “Computer systems, dating back to 1960s, have been developing to automatically solve math word problems (MWPs) (Feigenbaum and Feldman, 1963; Bobrow, 1964).”
Template-Based Math Word Problem Solvers with Recursive Neural Networks (2019, background): “Developing computer systems to automatically solve math word problems (MWPs) has been studied by NLP researchers since the 1960s (Feigenbaum and Feldman 1963; Bobrow 1964).”
Graph-to-Tree Learning for Solving Math Word Problems (2020, background): “Math Word Problem (MWP), which involves automatically answering a mathematical question according to a textual description, is an important natural language understanding task that has been studied by researchers since the 1960s (Bobrow, 1964).”
Deep Neural Network based system for solving Arithmetic Word problems (2017, background): “Designing algorithms to automatically solve math and science problems is a long-standing AI challenge (Bobrow, 1964).”