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Found 3 papers, 2 papers with code
Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale
A syntactic language model (SLM) incrementally generates a sentence with its syntactic tree in a left-to-right manner.
Augmenting Transformers with Recursively Composed Multi-grained Representations
More interestingly, the hierarchical structures induced by ReCAT exhibit strong consistency with human-annotated syntactic trees, indicating good interpretability brought by the CIO layers.
Constituency Grammar Induction
Natural Language Inference
+1
Domain Generalization Deep Graph Transformation
As a result, domain generalization graph transformation that predicts graphs not available in the training data is under-explored, with multiple key challenges to be addressed including (1) the extreme space complexity when training on all input-output mode combinations, (2) difference of graph topologies between the input and the output modes, and (3) how to generalize the model to (unseen) target domains that are not in the training data.
