Papers by Frank Drewes
Probing Multimodal Embeddings for Linguistic Properties: the Visual-Semantic Case (2020.coling-main)
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| Challenge: | Semantic embeddings have advanced the state of the art for natural language processing tasks . but their inner workings are poorly understood and there is a shortage of analysis tools . |
| Approach: | They propose to extend visual-semantic embeddings to multimodal domains by defining probing tasks for embeddable image-caption pairs and testing them with classifiers. |
| Outcome: | The proposed probing tasks show up to 16% more accurate on visual-semantic embeddings compared to unimodal embedders . the proposed extensions to multimodal domains have been lauded as promising in natural language processing . |
Bridging Perception, Memory, and Inference through Semantic Relations (2021.emnlp-main)
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| Challenge: | Recent studies suggest that it is impossible to learn meaning from surface form alone. |
| Approach: | They propose to develop triadic systems that combine neural and symbolic methods to provide a seamless information flow between them. |
| Outcome: | The proposed systems combine the strengths of neural and symbolic methods to achieve a seamless information flow between them. |
Dynamic Topic Modeling by Clustering Embeddings from Pretrained Language Models: A Research Proposal (2022.aacl-srw)
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| Challenge: | Neural Topic Models (NTMs) are topic models that are created with the help of a pretrained language model. |
| Approach: | They propose to do Neural Topic Modeling by Clustering document Embeddings (NTM-CE) with a pretrained language model to create dynamic topic models. |
| Outcome: | The proposed model can be evaluated theoretically and practically using quantitative measurements of coherence and human evaluation to evaluate the model. |