---
title: "Mova: frames → world — papers it applies — Elamur Research"
description: "9 papers Mova applies at the stage «frames → world»: Keeping the world of the text: entities, their states, quantities and compiled commonsense."
canonical: "https://research.elamur.ai/free/en/projects/mova/world/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/projects/mova/world/)

[home](https://md.research.elamur.ai/free/en/) / [projects](https://md.research.elamur.ai/free/en/projects/) / [Mova](https://md.research.elamur.ai/free/en/projects/mova/) / [Main list](https://md.research.elamur.ai/free/en/projects/mova/main/) / frames → world

Mova · papers by stage

# frames → world

Keeping the world of the text: entities, their states, quantities and compiled commonsense. The 9 works of [Mova](https://mova.elamur.ai/free/en/)'s own list that this stage uses; measured marks a work whose effect Mova measured in its own experiments (5 here). Each title opens our card: the summary, how Mova uses the work, its references and who cites it. What the stage does is on [Mova's pipeline page](https://mova.elamur.ai/free/en/); every stage is on [the main list](https://md.research.elamur.ai/free/en/projects/mova/main/).

- [Solving General Arithmetic Word Problems](https://md.research.elamur.ai/free/en/papers/roy-2015-solving-general-arithmetic-word-problems/)measured Subhro Roy, Dan Roth · 2015 **What Mova took:** `math/src/arith.rs` — the core of the arithmetic tree model: a relevance judgement for each number and an operation judgement for each pair of numbers at their lowest common ancestor, both as averaged perceptrons over word…
- [Unit Dependency Graph and its Application to Arithmetic Word Problem Solving](https://md.research.elamur.ai/free/en/papers/roy-2017-unit-dependency-graph-arithmetic/)measured Subhro Roy, Dan Roth · 2017 **What Mova took:** `global/seeds/shortcuts/units.md`: a first-level knowledge "seed" that states the core rule (a "X per Y" quantity is a rate; total = rate × number of units; equal units combine only by addition/subtraction) and links concepts…
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://md.research.elamur.ai/free/en/papers/hwang-2020-comet-atomic-2020-symbolic-neural/)measured Jena D. Hwang et al. · 2020 (arXiv) / 2021 (AAAI) **What Mova took:** Mova uses the ATOMIC 2020 data (CC BY 4.0), not the neural COMET model. Small SQL extractions (`global/data/atomic-feelings.sql`, `global/data/atomic-uses.sql`) produce compiled first-level seeds. No knowledge model is trained.
- [Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction](https://md.research.elamur.ai/free/en/papers/jie-2022-learning-reason-deductively-math-word/)measured Zhanming Jie, Jierui Li, Wei Lu · 2022 **What Mova took:** `math/src/deduct.rs` (math solver v5, `mathsolve steps --dag`): a transparent reimplementation of the DeductReasoner idea as a graph of subproblems (DAG). The pool holds the numbers from the text plus all intermediate results…
- [Cutting Through the Noise: Boosting LLM Performance on Math Word Problems](https://md.research.elamur.ai/free/en/papers/anantheswaran-2024-cutting-through-noise-boosting-llm/)measured Ujjwala Anantheswaran et al. · 2024 **What Mova took:** Inspired the `--augment` option of the math solver training in math/src/bin/mathsolve.rs: every training problem gets a copy with a sentence containing a number from another problem inserted just before the question, while the…
- [Categorization and Representation of Physics Problems by Experts and Novices](https://md.research.elamur.ai/free/en/papers/chi-1981-categorization-representation-physics-problems-experts/) Michelene T. H. Chi, Paul J. Feltovich, Robert Glaser · 1981 **What Mova took:** `world/src/quant.rs` — the quantitative world layer for text word problems: each state change in a script is tied to a sentence (`@N`) and labelled with the principle it represents…
- [Learning to Solve Arithmetic Word Problems with Verb Categorization](https://md.research.elamur.ai/free/en/papers/hosseini-2014-learning-solve-arithmetic-word-problems/) Mohammad Javad Hosseini et al. · 2014 **What Mova took:** `math/src/steps.rs`: the math solver's main non-lexical features are verb classes (have, get, give, make, lose, move, compare) in the spirit of ARIS's verb categorisation, extended with Roy & Roth 2018. The model sees "how the…
- [Learning To Use Formulas To Solve Simple Arithmetic Problems](https://md.research.elamur.ai/free/en/papers/mitra-2016-learning-use-formulas-solve-simple/) Arindam Mitra, Chitta Baral · 2016 **What Mova took:** `global/seeds/shortcuts/units.md` — the comparison formula is the source of a first-level link "compare → subtract": questions of the form "how many more / fewer" are explained as a difference, i.e. subtraction.
- [Mapping to Declarative Knowledge for Word Problem Solving](https://md.research.elamur.ai/free/en/papers/roy-2018-mapping-declarative-knowledge-word-problem/) Subhro Roy, Dan Roth · 2018 **What Mova took:** `global/seeds/shortcuts/possession.md`: a first-level knowledge seed with verb classes `have`, `get`, `give`, `make`, `lose`, `move`…