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Found 7 papers, 2 papers with code
Enhancing Question Answering by Injecting Ontological Knowledge through Regularization
Deep neural networks have demonstrated high performance on many natural language processing (NLP) tasks that can be answered directly from text, and have struggled to solve NLP tasks requiring external (e. g., world) knowledge.
Flight of the PEGASUS? Comparing Transformers on Few-shot and Zero-shot Multi-document Abstractive Summarization
Recent work has shown that pre-trained Transformers obtain remarkable performance on many natural language processing tasks including automatic summarization.
Towards Zero-Shot Conditional Summarization with Adaptive Multi-Task Fine-Tuning
Automatic summarization research has traditionally focused on providing high quality general-purpose summaries of documents.
Embedding Open-domain Common-sense Knowledge from Text
Building a knowledge graph for representing common-sense knowledge in which concepts discerned from noun phrases are cast as vertices and lexicalized relations are cast as edges leads to learning the embeddings of common-sense knowledge accounting for semantic compositionality as well as implied knowledge.
Clinical Data-Driven Probabilistic Graph Processing
To perform inference on the graphical model, we describe a technique of smoothing the conditional likelihood of medical concepts by their semantically-similar belief values.
Annotating Spatial Containment Relations Between Events
Events form complex predicate-argument structures that model the participants in the event, their roles, as well as the temporal and spatial grounding.
