Papers by Nina Schneidermann
Probing for Hyperbole in Pre-Trained Language Models (2023.acl-srw)
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| Challenge: | Hyperbole is a common figure of speech that involves the use of exaggerated language for emphasis or effect. |
| Approach: | They conduct edge and minimal description length probing experiments on three pre-trained language models to explore the extent to which hyperbolic information is encoded . they also annotate 63 hyperbole sentences from the HYPO dataset according to an operational taxonomy to conduct an error analysis to explore encoding of different hyperboli categories. |
| Outcome: | The results show that hyperbole is encoded in a limited extent in pre-trained models and mostly in the final layers. |
Towards a Gold Standard for Evaluating Danish Word Embeddings (2020.lrec-1)
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| Challenge: | Existing word embedding models resemble semantic similarity solely by distribution, but there seems to be a need for future judgments to measure similarity in full context and along more than a single spectrum. |
| Approach: | They propose a model-agnostic similarity goal standard for evaluating Danish word embeddings based on human judgments made by 42 native speakers of Danish. |
| Outcome: | The goal standard is applied to evaluate Danish word embeddings on 42 native speakers of Danish. |