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Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractNeural natural language generation (NLG) and understanding (NLU) models are data-hungry and require massive amounts of annotated data to be competitive. Recent frameworks address this bottleneck with generative models that …...

42790

Improving Compositional Generalization with Self-Training for Data-to-Text Generation

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractData-to-text generation focuses on generating fluent natural language responses from structured meaning representations (MRs). Such representations are compositional and it is costly to collect responses for all possible combinations of …...

39090