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Found 7 papers, 4 papers with code
Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary
A desirable property of a reference-based evaluation metric that measures the content quality of a summary is that it should estimate how much information that summary has in common with a reference.
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Bayesian neural networks (BNN) and deep ensembles are principled approaches to estimate the predictive uncertainty of a deep learning model.
Faithful Embeddings for Knowledge Base Queries
We address this problem with a novel QE method that is more faithful to deductive reasoning, and show that this leads to better performance on complex queries to incomplete KBs.
How Large Are Lions? Inducing Distributions over Quantitative Attributes
Most current NLP systems have little knowledge about quantitative attributes of objects and events.
PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text
We focus on a setting in which a corpus is supplemented with a large but incomplete KB, and on questions that require non-trivial (e. g., ``multi-hop'') reasoning.
Incremental Reading for Question Answering
Current state-of-the-art question answering models reason over an entire passage, not incrementally.
