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Found 5 papers, 1 papers with code
Low-resource Taxonomy Enrichment with Pretrained Language Models
Conventional supervised methods for this enrichment task fail to find optimal parents of new terms in low-resource settings where only small taxonomies are available because of overfitting to hierarchical relationships in the taxonomies.
Context Quality Matters in Training Fusion-in-Decoder for Extractive Open-Domain Question Answering
Finally, based on these observations, we propose a method to mitigate overfitting to specific context quality by introducing bias to the cross-attention distribution, which we demonstrate to be effective in improving the performance of FiD models on different context quality.
Learning Directly from Grammar Compressed Text
Neural networks using numerous text data have been successfully applied to a variety of tasks.
Cross-Sentence N-ary Relation Extraction using Lower-Arity Universal Schemas
Most existing relation extraction approaches exclusively target binary relations, and n-ary relation extraction is relatively unexplored.
Knowledge-Based Distant Regularization in Learning Probabilistic Models
Exploiting the appropriate inductive bias based on the knowledge of data is essential for achieving good performance in statistical machine learning.
