Papers with learning language
Learning Language through Grounding (2025.naacl-tutorial)
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| Challenge: | This tutorial provides a historical overview of grounding and discusses its use in computational linguistics and in computational language processing. |
| Approach: | They introduce the concept of grounding and discuss future directions and open challenges . they will delve into recent progress in learning lexical semantics, syntax, and complex meanings through various forms of ground. |
| Outcome: | This course will provide an overview of the field of grounding and discuss future directions and challenges related to large language models and scaling. |
What do Models Learn From Training on More Than Text? Measuring Visual Commonsense Knowledge (2022.acl-srw)
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| Challenge: | Existing evaluation methods to measure what language models learn from multimodal training are lacking. |
| Approach: | They propose two evaluation tasks to measure commonsense knowledge in language models by using visual data to evaluate multimodal models and unimodal baselines. |
| Outcome: | The proposed evaluation tasks show that training on a visual modality improves on the visual commonsense knowledge in language models. |
Is Word Segmentation Child’s Play in All Languages? (P19-1)
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| Challenge: | Existing word learning strategies for infants are cross-linguistically robust . infants do not know which language(s) will be found in their environment at the beginning of development . |
| Approach: | They propose to use 11 conceptually diverse algorithms to learn word-like units in infants . they propose to employ cross-linguistically robust algorithms that can be used by all infants. |
| Outcome: | The proposed algorithms perform above chance on 8 different languages . the results show that some of the algorithms are cross-linguistically valid . |
How to Adapt Pre-trained Vision-and-Language Models to a Text-only Input? (2022.coling-1)
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| Challenge: | Current language models have been criticised for learning language from text alone without connection between words and their meaning. |
| Approach: | They propose to train models on more sources than text to provide the lacking connection between words and their meanings. |
| Outcome: | The proposed model adaptation methods perform differently for different models and unimodal model counterparts perform on par with the VL models regardless of adaptation. |