How Large Are Lions? Inducing Distributions over Quantitative Attributes

By Yanai Elazar, A. Mahabal, Deepak Ramachandran, Tania Bedrax-Weiss, Dan Roth. 2019

61 citations recorded by the source corpus.

Most current NLP systems have little knowledge about quantitative attributes of objects and events. We propose an unsupervised method for collecting quantitative information from large amounts of web data, and use it to create a new, very large resource consisting of distributions over physical quantities associated with objects, adjectives, and verbs which we call Distributions over Quantitative (DoQ). This contrasts with recent work in this area which has focused on making only relative comparisons such as “Is a lion bigger than a wolf?”. Our evaluation shows that DoQ compares favorably with state of the art results on existing datasets for relative comparisons of nouns and adjectives, and on a new dataset we introduce.

Subfields: Natural Language Processing

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Citations in the corpus

Experience Grounds Language (2020, background): “WS2 may also capture that oranges and baseballs both roll, but is unlikely to capture the deformation strength, surface texture, or relative sizes of these objects (Elazar et al., 2019).”
Temporal Common Sense Acquisition with Minimal Supervision (2020, background): “These works include but are not limited to size, weight, and strength (Bagherinezhad et al., 2016; Forbes and Choi, 2017; Elazar et al., 2019), roundness and deli-ciousness (Yang et al., 2018), and intensity (Cocos et al., 2018).”
Do Language Embeddings capture Scales? (2020, methodology): “For F&C Cleaned dataset, we use the data and the train/dev/test splits from (Elazar et al., 2019).”
WinoWhy: A Deep Diagnosis of Essential Commonsense Knowledge for Answering Winograd Schema Challenge (2020, background): “, 2019), reasoning (Schubert, 2015), property (Liu and Singh, 2004), and quantity (Elazar et al., 2019), and has been proven crucial in many downstream tasks like question answering (Lin et al.”
TransOMCS: From Linguistic Graphs to Commonsense Knowledge (2020, background): “…covers a variety of knowledge types like knowledge about typical location or causes of events in OMCS [ Liu and Singh, 2004 ] , events causes, effects, and temporal properties in ATOMIC [ Sap et al. , 2019 ] and [Zhou et al. , 2020], and physical attributes of objects [Elazar et al. , 2019].”
Representing Numbers in NLP: a Survey and a Vision (2021, background): “DoQ (Elazar et al., 2019) is a web-extracted dataset of Distributions over Quantities, which can be used as a benchmark for language models’ measurement estimation abilities (Zhang et al.”
PROST: Physical Reasoning about Objects through Space and Time (2021, background): “Other datasets (Forbes and Choi, 2017; Elazar et al., 2019; Goel et al., 2019) focus on object– attribute comparison.”
Do Language Embeddings capture Scales? (2020, methodology): “Our primary resource for the scalar probing task is Distributions over Quantities (DoQ; Elazar et al., 2019) which consists of empirical counts of scalar attribute values associated with >350K nouns, adjectives, and verbs over 10 different attributes, collected from web data.”