Timo Schick

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Exploiting Cloze Questions for Few-Shot Text Classification and Natural Language Inference pdf

Hinrich Schutze, Timo Schick, 2020, ArXiv.

BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance

Hinrich Schütze, Timo Schick, 2020, ACL.

Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification

Helmut Schmid, Hinrich Schutze, Timo Schick, 2020, COLING.

Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts pdf

Hinrich Schütze, Timo Schick, 2019, NAACL-HLT.

Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking

Hinrich Schütze, Timo Schick, Hinrich Schütze, 2019, AAAI.

Transition-Based Generation from Abstract Meaning Representations pdf

Timo Schick, 2017, ArXiv.

It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners pdf

Hinrich Schutze, Timo Schick, Hinrich Schütze, 2020, NAACL.

Few-Shot Text Generation with Pattern-Exploiting Training pdf

Hinrich Schutze, Timo Schick, Hinrich Schütze, 2020, ArXiv.

True Few-Shot Learning with Prompts—A Real-World Perspective

Timo Schick, Hinrich Schutze, Hinrich Schütze, 2021, Transactions of the Association for Computational Linguistics.

Generating Datasets with Pretrained Language Models

Timo Schick, Hinrich Schutze, Hinrich Schütze, 2021, EMNLP.

Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference

Timo Schick, Hinrich Schutze, Hinrich Schütze, 2020, EACL.

Few-Shot Text Generation with Natural Language Instructions

Hinrich Schütze, Timo Schick, Hinrich Schütze, 2020, EMNLP.

Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP

Timo Schick, Sahana Udupa, Hinrich Schutze, 2021, Transactions of the Association for Computational Linguistics.