Joe Stacey

According to our database, Joe Stacey authored at least 10 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

LUCID: LLM-Generated Utterances for Complex and Interesting Dialogues.

Joe Stacey, Jianpeng Cheng, John Torr, Tristan Guigue, Joris Driesen, Alexandru Coca, Mark Gaynor, Anders Johannsen

CoRR, 2024

2023

Logical Reasoning for Natural Language Inference Using Generated Facts as Atoms.

Joe Stacey, Pasquale Minervini, Haim Dubossarsky, Oana-Maria Camburu, Marek Rei

CoRR, 2023

Improving Robustness in Knowledge Distillation Using Domain-Targeted Data Augmentation.

Joe Stacey, Marek Rei

CoRR, 2023

When and Why Does Bias Mitigation Work?

Abhilasha Ravichander, Joe Stacey, Marek Rei

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

2022

Logical Reasoning with Span Predictions: Span-level Logical Atoms for Interpretable and Robust NLI Models.

Joe Stacey, Pasquale Minervini, Haim Dubossarsky, Marek Rei

CoRR, 2022

Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI Models.

Joe Stacey, Pasquale Minervini, Haim Dubossarsky, Marek Rei

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Supervising Model Attention with Human Explanations for Robust Natural Language Inference.

Joe Stacey, Yonatan Belinkov, Marek Rei

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Natural Language Inference with a Human Touch: Using Human Explanations to Guide Model Attention.

Joe Stacey, Yonatan Belinkov, Marek Rei

CoRR, 2021

2020

There is Strength in Numbers: Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training.

Joe Stacey, Pasquale Minervini, Haim Dubossarsky, Sebastian Riedel, Tim Rocktäschel

CoRR, 2020

Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training.

Joe Stacey, Pasquale Minervini, Haim Dubossarsky, Sebastian Riedel, Tim Rocktäschel

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020