| Challenge: | Existing models cannot capture numeracy, but they can be useful for complex reasoning tasks. |
| Approach: | They investigate numerical reasoning capabilities of a question-answering model . they probe token embedding methods on synthetic list maximum, number decoding, and addition tasks. |
| Outcome: | The proposed model excels on questions that require numerical reasoning, i.e., it already captures numeracy. |
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Exploring Numeracy in Word Embeddings (P19-1)
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| Challenge: | Existing word embeddings are inadequate at capturing numerical properties of numbers. |
| Approach: | They propose to use word embeddings to capture numerical properties of numbers . they hope to develop methods which better capture numeric properties . |
| Outcome: | The proposed models lack the ability to capture numeric properties of numbers, the authors show . their findings provide a starting point for the development of better models . |
Numeracy enhances the Literacy of Language Models (2021.emnlp-main)
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| Challenge: | Specialized number representations have shown improvements on numerical reasoning tasks like arithmetic word problems and masked number prediction. |
| Approach: | They propose to use six different number encoders to improve masked word prediction by avoiding conflating nominal and ordinal number occurrences. |
| Outcome: | The proposed representations improve masked word prediction accuracy and generalize to contexts without annotated numbers. |
Revealing the Numeracy Gap: An Empirical Investigation of Text Embedding Models (2026.findings-eacl)
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| Challenge: | Text embedding models are widely used in natural language processing but are often benchmarked on tasks that do not require understanding nuanced numerical information in text. |
| Approach: | They evaluate 13 widely used text embedding models and find they struggle to capture numerical details accurately. |
| Outcome: | The proposed models struggle to capture nuanced numerical details accurately, despite being benchmarked on tasks that do not require understanding nuance. |
Learning Numeracy: A Simple Yet Effective Number Embedding Approach Using Knowledge Graph (2021.findings-emnlp)
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| Challenge: | Existing models for numeracy-intensive applications fail to learn numerability . existing models fail to handle numbers, resulting in performance problems . |
| Approach: | They propose a number embedding approach that embeds numbers into dimensional space . they construct a knowledge graph consisting of number entities and magnitude relations . |
| Outcome: | The proposed method is easy to implement and shows that it performs well on numeracy-related tasks. |
Representing Numbers in NLP: a Survey and a Vision (2021.naacl-main)
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| Challenge: | Numeracy is an essential skill for language understanding since numbers are often interspersed in text. |
| Approach: | They propose a comprehensive taxonomy of tasks and methods to represent numbers in text . they synthesize best practices for representing numbers in texts and articulate a vision for holistic numeracy . |
| Outcome: | The proposed model synthesizes best practices for representing numbers in text . it argues that the model is more effective than other approaches . |
Laying Anchors: Semantically Priming Numerals in Language Modeling (2024.findings-naacl)
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| Challenge: | Numeracy is the comprehension of numbers, and numerals are important for comprehension. |
| Approach: | They propose methods to semantically prime numerals by generating anchors governed by the distribution of numeral in any corpus. |
| Outcome: | The proposed methods perform better on numeracy tasks for both in-domain and out-domain numerals. |
Numeracy for Language Models: Evaluating and Improving their Ability to Predict Numbers (P18-1)
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| Challenge: | Numeracy is the ability to understand and work with numbers. |
| Approach: | They propose a neural architecture that uses a continuous probability density function to model numerals from an open vocabulary using hierarchical models. |
| Outcome: | The proposed model reduces errors by 18% and 54% on clinical and scientific datasets compared to the second best model for each dataset . |
Methods for Numeracy-Preserving Word Embeddings (2020.emnlp-main)
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Dhanasekar Sundararaman, Shijing Si, Vivek Subramanian, Guoyin Wang, Devamanyu Hazarika, Lawrence Carin
| Challenge: | Word embedding models capture semantic relationships between words but fail to capture numerical properties associated with numbers. |
| Approach: | They propose a method to assign and learn embeddings for numbers using word embedders. |
| Outcome: | The proposed model outperforms pre-trained word embedding models across multiple examples of two tasks. |
Do Language Embeddings capture Scales? (2020.findings-emnlp)
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| Challenge: | Pretrained Language Models possess significant linguistic, common sense and factual knowledge, but are short of the capability required for general common-sense reasoning. |
| Approach: | They propose to train pretrained language models with a method of canonicalizing numbers . they address a task which is also pre-requisite for general common-sense reasoning . |
| Outcome: | The proposed model can answer questions about common sense and linguistics, but lacks the capability to answer questions on scalar attributes. |
Do Language Models Understand Measurements? (2022.findings-emnlp)
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| Challenge: | Existing studies on numerical reasoning over text (NRoT) tests PLMs to understand numbers in contexts where numbers are an integral part of the context. |
| Approach: | They propose a simple embedding strategy to better distinguish between numbers and units, which leads to a significant improvement in probing tasks. |
| Outcome: | The proposed model distinguishes between numbers and units, which leads to significant improvement in probing tasks. |