LEARNING UNCERTAINTY FOR UNKNOWN DOMAINS WITH ZERO-TARGET-ASSUMPTION

Yu Yu, Hassan Sajjad, Jia Xu

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

We introduce our Maximum-Entropy Rewarded Reinforcement Learning (MERRL) framework that selects training data for more accurate Natural Language Processing (NLP).Because conventional data selection methods select training samples based on the test domain knowledge and not on real life data, they frequently fail in unknown domains like patent and Twitter.Our approach selects training samples that maximize information uncertainty measured by entropy, including observation entropy like empirical Shannon entropy, Min-entropy, Rényi entropy, and prediction entropy using mutual information, to cover more possible queries that may appear in unknown worlds.Our MERRL using regularized A2C and SAC achieves up to -99.7 perplexity decrease (-43.4% relatively) in language modeling, +25.0 accuracy increase (+40.0% relatively) in sentiment analysis, and +5.0 F1 score increase (+30.8% relatively) in named entity recognition over various domains, demonstrating strong generalization power on unknown test sets.

Original languageEnglish
StatePublished - 2023
Event11th International Conference on Learning Representations, ICLR 2023 - Kigali, Rwanda
Duration: 1 May 20235 May 2023

Conference

Conference11th International Conference on Learning Representations, ICLR 2023
Country/TerritoryRwanda
CityKigali
Period1/05/235/05/23

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