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Found 12 papers, 4 papers with code
Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering
We validate the performance of our KAPING framework on the knowledge graph question answering task, that aims to answer the user's question based on facts over a knowledge graph, on which ours outperforms relevant zero-shot baselines by up to 48% in average, across multiple LLMs of various sizes.
CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing
A bottleneck to developing Semantic Parsing (SP) models is the need for a large volume of human-labeled training data.
Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering
We introduce Mintaka, a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models.
End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs
Our model only needs the question text and the answer entities to train, and delivers a stand-alone QA model that does not require an additional ER component to be supplied during runtime.
Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection
End-to-end question answering using a differentiable knowledge graph is a promising technique that requires only weak supervision, produces interpretable results, and is fully differentiable.
Relation Extraction from Tables using Artificially Generated Metadata
To address this issue, we propose methods to artificially create some of this metadata for synthetic tables.
Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity
Our generated text has a significantly better semantic fidelity than the state of the art across all four datasets
Ranked #1 on
Data-to-Text Generation
on ViGGO
What do Models Learn from Question Answering Datasets?
While models have reached superhuman performance on popular question answering (QA) datasets such as SQuAD, they have yet to outperform humans on the task of question answering itself.
Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs
In this work, we provide a new formulation for Graph Convolutional Neural Networks (GCNNs) for link prediction on graph data that addresses common challenges for biomedical knowledge graphs (KGs).
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation
Generating novel molecules with optimal properties is a crucial step in many industries such as drug discovery.
Alternating Decision Forests
Contrary to Boosted Trees, in our method the loss minimization is an inherent part of the tree growing process, thus allowing to keep the benefits of common Random Forests, such as, parallel processing.
Learning Anchor Planes for Classification
It provides a set of anchor points which form a local coordinate system, such that each data point on the manifold can be approximated by a linear combination of its anchor points, and the linear weights become the local coordinate coding.
