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Found 4 papers, 2 papers with code
Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Models
We systematically study the effects of the phenomenon across NLI models for $\textbf{in-}$ and $\textbf{out-of-}$ domain settings.
Approximate Answering of Graph Queries
We will first provide an overview of the different query types which can be supported by these methods and datasets typically used for evaluation, as well as an insight into their limitations.
Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection
The results show that our method outperforms the state-of-the-art with an average of $3. 5$ F1 points increase in-domain, and is more generalizable with an averaged increase of $10. 2$ F1 on out-of-domain evaluation while using $\leq10\%$ of the training data.
Ranked #1 on
Stance Detection
on mtsd
Complex Query Answering with Neural Link Predictors
Finally, we demonstrate that it is possible to explain the outcome of our model in terms of the intermediate solutions identified for each of the complex query atoms.
Ranked #1 on
Complex Query Answering
on NELL995
