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Found 6 papers, 4 papers with code
RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction
However, existing works often struggle to obtain class prototypes with accurate relational semantics: 1) To build prototype for a target relation type, they aggregate the representations of all entity pairs holding that relation, while these entity pairs may also hold other relations, thus disturbing the prototype.
Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing
Based on these observations, we propose a method named \texttt{p-align} to improve the compositional generalization of Text-to-SQL models.
Enhancing Cross-lingual Natural Language Inference by Soft Prompting with Multilingual Verbalizer
In this paper, we propose a novel Soft prompt learning framework with the Multilingual Verbalizer (SoftMV) for XNLI.
Cross-Lingual Natural Language Inference
Cross-Lingual Transfer
Gaussian Prior Reinforcement Learning for Nested Named Entity Recognition
Existing works for nested NER ignore the recognition order and boundary position relation of nested entities.
Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution
We propose the first character-level white-box adversarial attack method against transformer models.
Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction
Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce.
