Solving General Arithmetic Word Problems

This paper presents a novel approach to automatically solving arithmetic word problems. This is the first algorithmic approach that can handle arithmetic problems with multiple steps and operations, without depending on additional annotations or predefined templates. We develop a theory for expression trees that can be used to represent and evaluate the target arithmetic expressions; we use it to uniquely decompose the target arithmetic problem to multiple classification problems; we then compose an expression tree, combining these with world knowledge through a constrained inference framework. Our classifiers gain from the use of {\em quantity schemas} that supports better extraction of features. Experimental results show that our method outperforms existing systems, achieving state of the art performance on benchmark datasets of arithmetic word problems.Comment: EMNLP 201

Reasoning about quantities in natural language

Quantitative reasoning involves understanding the use of quantities and numeric relations in text, and reasoning with respect to them. It forms an essential part of everyday interaction. However, little work from the Natural Language Processing community has focused on quantitative reasoning. In this thesis, we investigate the challenges in performing automated quantitative reasoning over natural language text. We formulate several tasks to tackle some of the fundamental problems of quantitative reasoning, and address the problem of developing robust statistical methods for these tasks. We show that standard NLP tools are not sufficient to obtain the abstraction needed for quantitative reasoning; the standard NLP pipeline needs to be extended in various ways. We propose several technical ideas for these extensions. We first look at the problem of detecting and normalizing quantities expressed in free form text, and show that correct detection and normalization can support several simple quantitative inferences. We then focus on numeric relation extraction from sentences, and show that several natural properties of language can be leveraged to effectively extract numeric relations from a sentence. We finally investigate the problem of quantitative reasoning over multiple quantities mentioned across several sentences. We develop a decomposition strategy which allows reasoning over pairs of numbers to be combined effectively to perform global reasoning. We also look at the problem of effectively using math domain knowledge in quantitative reasoning. On this front, we first propose graph representations called "unit dependency graphs'', and show that these graph representations can be used to effectively incorporate dimensional analysis knowledge in quantitative reasoning. Next, we develop a general framework to incorporate any declarative knowledge into quantitative reasoning. This framework is used to incorporate several mathematical concepts into textual quantitative reasoning, leading to robust reasoning systems

Solving General Arithmetic Word Problems

Abstract This paper presents a novel approach to automatically solving arithmetic word problems. This is the first algorithmic approach that can handle arithmetic problems with multiple steps and operations, without depending on additional annotations or predefined templates. We develop a theory for expression trees that can be used to represent and evaluate the target arithmetic expressions; we use it to uniquely decompose the target arithmetic problem to multiple classification problems; we then compose an expression tree, combining these with world knowledge through a constrained inference framework. Our classifiers gain from the use of quantity schemas that supports better extraction of features. Experimental results show that our method outperforms existing systems, achieving state of the art performance on benchmark datasets of arithmetic word problems

InstructExcel: A Benchmark for Natural Language Instruction in Excel

With the evolution of Large Language Models (LLMs) we can solve increasingly more complex NLP tasks across various domains, including spreadsheets. This work investigates whether LLMs can generate code (Excel OfficeScripts, a TypeScript API for executing many tasks in Excel) that solves Excel specific tasks provided via natural language user instructions. To do so we introduce a new large-scale benchmark, InstructExcel, created by leveraging the 'Automate' feature in Excel to automatically generate OfficeScripts from users' actions. Our benchmark includes over 10k samples covering 170+ Excel operations across 2,000 publicly available Excel spreadsheets. Experiments across various zero-shot and few-shot settings show that InstructExcel is a hard benchmark for state of the art models like GPT-4. We observe that (1) using GPT-4 over GPT-3.5, (2) providing more in-context examples, and (3) dynamic prompting can help improve performance on this benchmark.Comment: Findings of EMNLP 2023, 18 page

Unit Dependency Graph and Its Application to Arithmetic Word Problem Solving

Math word problems provide a natural abstraction to a range of natural language understanding problems that involve reasoning about quantities, such as interpreting election results, news about casualties, and the financial section of a newspaper. Units associated with the quantities often provide information that is essential to support this reasoning. This paper proposes a principled way to capture and reason about units and shows how it can benefit an arithmetic word problem solver. This paper presents the concept of Unit Dependency Graphs (UDGs), which provides a compact representation of the dependencies between units of numbers mentioned in a given problem. Inducing the UDG alleviates the brittleness of the unit extraction system and allows for a natural way to leverage domain knowledge about unit compatibility, for word problem solving. We introduce a decomposed model for inducing UDGs with minimal additional annotations, and use it to augment the expressions used in the arithmetic word problem solver of (Roy and Roth 2015) via a constrained inference framework. We show that introduction of UDGs reduces the error of the solver by over 10 %, surpassing all existing systems for solving arithmetic word problems. In addition, it also makes the system more robust to adaptation to new vocabulary and equation forms

Leveraging Past References for Robust Language Grounding

© 2019 Association for Computational Linguistics. Grounding referring expressions to objects in an environment has traditionally been considered a one-off, ahistorical task. However, in realistic applications of grounding, multiple users will repeatedly refer to the same set of objects. As a result, past referring expressions for objects can provide strong signals for grounding subsequent referring expressions. We therefore reframe the grounding problem from the perspective of coreference detection and propose a neural network that detects when two expressions are referring to the same object. The network combines information from vision and past referring expressions to resolve which object is being referred to. Our experiments show that detecting referring expression coreference is an effective way to ground objects described by subtle visual properties, which standard visual grounding models have difficulty capturing. We also show the ability to detect object coreference allows the grounding model to perform well even when it encounters object categories not seen in the training data

Multimodal estimation and communication of latent semantic knowledge for robust execution of robot instructions

The goal of this article is to enable robots to perform robust task execution following human instructions in partially observable environments. A robot’s ability to interpret and execute commands is fundamentally tied to its semantic world knowledge. Commonly, robots use exteroceptive sensors, such as cameras or LiDAR, to detect entities in the workspace and infer their visual properties and spatial relationships. However, semantic world properties are often visually imperceptible. We posit the use of non-exteroceptive modalities including physical proprioception, factual descriptions, and domain knowledge as mechanisms for inferring semantic properties of objects. We introduce a probabilistic model that fuses linguistic knowledge with visual and haptic observations into a cumulative belief over latent world attributes to infer the meaning of instructions and execute the instructed tasks in a manner robust to erroneous, noisy, or contradictory evidence. In addition, we provide a method that allows the robot to communicate knowledge dissonance back to the human as a means of correcting errors in the operator’s world model. Finally, we propose an efficient framework that anticipates possible linguistic interactions and infers the associated groundings for the current world state, thereby bootstrapping both language understanding and generation. We present experiments on manipulators for tasks that require inference over partially observed semantic properties, and evaluate our framework’s ability to exploit expressed information and knowledge bases to facilitate convergence, and generate statements to correct declared facts that were observed to be inconsistent with the robot’s estimate of object properties.ARO (Grant W911NF-15-1-0402)Toyota Research Institute (Award LPC000765- SR

Intelligently designed fly-ash based hybrid composites with very high hardness and Young's modulus

Currently, India generates annually about 112 million tones of fly ash (FA), as an industrial waste from thermal power plants. As part of the global journey to convert waste to wealth here we report the intelligent design based synthesis of FA based hybrid composites with spectacular improvement in Young's modulus and nanohardness. The novel design approach utilized alkali activation as well as simultaneous reinforcements of the porous FA matrix with a layered white china clay (WCC) and chopped E glass fiber. The developed materials were subsequently characterized by nanoindentation technique, pH measurement, alkali dissolution, FESEM, etc. techniques to evolve the structure-property correlation. The optimized design and optimal alkali activation lead to achievements of about 233% and 545% enhancements in Young's modulus and hardness, respectively. These results are rationalized in terms of chemical analysis, Si:Al ratio, presence of silicate network modifiers e.g., Na2O and CaO, microstructure, density, extent of polymerization due to alkali activation, processing condition and elastic recovery as well as the ratio of energy spent in elastic and plastic deformations during the nanoindentation processes. Finally, a schematic model is proposed to explain the experimental observations. (C) 2017 Elsevier Ltd. All rights reserved