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Found 4 papers, 2 papers with code
Structured Language Generation Model for Robust Structure Prediction
Previous work in structured prediction (e. g. NER, information extraction) using single model make use of explicit dataset information, which helps boost in-distribution performance but is orthogonal to robust generalization in real-world situations.
Punctuation Restoration Improves Structure Understanding without Supervision
Unsupervised learning objectives like language modeling and de-noising constitute a significant part in producing pre-trained models that perform various downstream applications from natural language understanding to conversational tasks.
Syntactic Data Augmentation Increases Robustness to Inference Heuristics
Pretrained neural models such as BERT, when fine-tuned to perform natural language inference (NLI), often show high accuracy on standard datasets, but display a surprising lack of sensitivity to word order on controlled challenge sets.
BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance
If the same neural network architecture is trained multiple times on the same dataset, will it make similar linguistic generalizations across runs?
