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Found 4 papers, 3 papers with code
ZGUL: Zero-shot Generalization to Unseen Languages using Multi-source Ensembling of Language Adapters
We posit that for more effective cross-lingual transfer, instead of just one source LA, we need to leverage LAs of multiple (linguistically or geographically related) source languages, both at train and test-time - which we investigate via our novel neural architecture, ZGUL.
Towards Fair and Calibrated Models
Further, we show that ensuring group-wise calibration with respect to the sensitive attributes automatically results in a fair model under our definition.
PARE: A Simple and Strong Baseline for Monolingual and Multilingual Distantly Supervised Relation Extraction
The contextual embeddings of tokens are aggregated using attention with the candidate relation as query -- this summary of whole passage predicts the candidate relation.
IMoJIE: Iterative Memory-Based Joint Open Information Extraction
While traditional systems for Open Information Extraction were statistical and rule-based, recently neural models have been introduced for the task.
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