| Challenge: | Existing studies on language models for the extraction of semantic relations have focused on injecting semantic knowledge into these models to enhance them. |
| Approach: | They propose to extract lexical semantic relations from a BERT model and inject them into it using unsupervised methods based on semantic similarity at word and sentence levels. |
| Outcome: | The proposed method allows to enrich a BERT model without using any external semantic resource. |
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| Challenge: | contextual language models are dominant in the field of Natural Language Processing, but they are not suitable for all uses. |
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Probing Pretrained Language Models for Lexical Semantics (2020.emnlp-main)
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| Challenge: | BERT is a language representation model that has performed well in diverse language understanding benchmarks. |
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| Challenge: | Existing studies show that individual BERT heads encode particular dependency relation types, but they do not match one-to-one. |
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Finding Universal Grammatical Relations in Multilingual BERT (2020.acl-main)
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| Challenge: | a recent advent of pretrained language models has sparked a revolution in NLP . but, there are still questions about whether current approaches capture explicit, symbolic meaning . this tutorial will review efforts to tackle three key open problems in lexical and sentence-level semantics . |
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From BERT‘s Point of View: Revealing the Prevailing Contextual Differences (2022.findings-acl)
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| Challenge: | BERTology is a new approach to understanding the inner workings of large pretraining language models. |
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