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Found 2 papers, 1 papers with code
Permutation Invariant Strategy Using Transformer Encoders for Table Understanding
Existing methods that leverage pretrained Transformer encoders range from a simple construction of pseudo-sentences by concatenating text across rows or columns to complex parameter-intensive models that encode table structure and require additional pretraining.
Open Knowledge Graphs Canonicalization using Variational Autoencoders
In this work, we propose Canonicalizing Using Variational Autoencoders (CUVA), a joint model to learn both embeddings and cluster assignments in an end-to-end approach, which leads to a better vector representation for the noun and relation phrases.
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