Grounding Gradable Adjectives through Crowdsourcing (L18-1)

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Challenge: Often, texts describe interactions using vague, high-level language . crowdsourcing is expensive and requires extensive literature review and time .
Approach: They propose a method for estimating concrete groundings for a set of gradable adjectives by crowdsourcing human intuitions and fitting a mixed effects model to the text.
Outcome: The proposed model can generalize to unseen data and has a predictive R 2 of 0.632 in general and 0.677 on a subset of high-frequency adjectives.

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Challenge: 77 submissions were received for the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) 4 of the 73 valid submissions received were either invalid or withdrawn by the authors.
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Challenge: 91 submissions were received, 10 of which were either invalid or withdrawn .
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Challenge: Scholarly work in this area uses toy worlds and synthetic linguistic data, but grounded language learning offers several practical and scientific advantages.
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Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts (2021.emnlp-tutorials)

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Challenge: EMNLP tutorials are lecture-based presentations that are presented at conferences around the world.
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