Papers with CDSMs
Lexical Substitution for Evaluating Compositional Distributional Models (N18-2)
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| Challenge: | Compositional Distributional Semantic Models (CDSMs) model the meaning of phrases and sentences in vector space. |
| Approach: | They propose to use lexical substitution to evaluate CDSMs by comparing a LexSub-annotated corpus with a manual LexSub annotation. |
| Outcome: | The proposed model outperforms simple component-wise CDSMs and performs on par with the context2vec LexSub model using the same context. |
Does CLIP Bind Concepts? Probing Compositionality in Large Image Models (2024.findings-eacl)
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| Challenge: | Large-scale neural network models combining text and images have made incredible progress in recent years, but to what extent they encode compositional representations of the concepts over which they operate remains an open question . |
| Approach: | They compare the performance of a large pretrained vision and language model (CLIP) to a set of three synthetic datasets designed to test concept binding. |
| Outcome: | The proposed model can encode compositional concepts and bind variables in a structure-sensitive way, e.g., differentiating ‘cube behind sphere’ from ‘cub behind cube’. |