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Found 6 papers, 3 papers with code
Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets
Surprisingly, while CD is a human-centered affair, no works have focused on building methods that both 1) output uncertainty estimates that can be verified by experts and 2) interact with those experts to iteratively refine CD.
Bayesian Structure Learning with Generative Flow Networks
In Bayesian structure learning, we are interested in inferring a distribution over the directed acyclic graph (DAG) structure of Bayesian networks, from data.
Predicting Attention Sparsity in Transformers
Transformers' quadratic complexity with respect to the input sequence length has motivated a body of work on efficient sparse approximations to softmax.
Learning Non-Monotonic Automatic Post-Editing of Translations from Human Orderings
Recent research in neural machine translation has explored flexible generation orders, as an alternative to left-to-right generation.
Unbabel's Participation in the WMT19 Translation Quality Estimation Shared Task
We present the contribution of the Unbabel team to the WMT 2019 Shared Task on Quality Estimation.
Translator2Vec: Understanding and Representing Human Post-Editors
The combination of machines and humans for translation is effective, with many studies showing productivity gains when humans post-edit machine-translated output instead of translating from scratch.
