Coreference decisions among event mentions and among co-occurring entity mentions are highly interdependent, thus motivating joint inference.
Several datasets exist for summarization with objective intents where, for each document and intent (e. g., “weather”), a single summary suffices for all users.
Recent advances in domain adaptation reveal that adversarial learning on deep neural networks can learn domain invariant features to reduce the shift between source and target domains.
When the similarity is measured by dot-product between dual-encoder vectors or $\ell_2$-distance, there already exist many scalable and efficient search methods.
Explanation methods have emerged as an important tool to highlight the features responsible for the predictions of neural networks.
no code implementations • 17 Feb 2022 • Nishant Yadav, Meytar Sorek-Hamer, Michael Von Pohle, Ata Akbari Asanjan, Adwait Sahasrabhojanee, Esra Suel, Raphael Arku, Violet Lingenfelter, Michael Brauer, Majid Ezzati, Nikunj Oza, Auroop R. Ganguly
Urban air pollution is a public health challenge in low- and middle-income countries (LMICs).
We provide methods for specifying -- via a SQL extension -- and processing stochastic package queries (SPQs), in order to solve optimization problems over uncertain data, right where the data resides.
Previous queries in the user session can provide useful context for the user's intent and can be leveraged to suggest auto-completions that are more relevant while adhering to the user's prefix.
In this paper, we introduce a model in which linking decisions can be made not merely by linking to a knowledge base entity but also by grouping multiple mentions together via clustering and jointly making linking predictions.
In climate and earth systems models, while governing equations follow from first principles and understanding of key processes has steadily improved, the largest uncertainties are often caused by parameterizations such as cloud physics, which in turn have witnessed limited improvements over the last several decades.
Thus we introduce an approach to supervised hierarchical clustering that smoothly interpolates between single, average, and complete linkage, and we give a training procedure that simultaneously learns a linkage function and a dissimilarity function.