Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training. - Joe Stacey; Pasquale Minervini; Haim Dubossarsky; Sebastian Riedel 001; Tim Rocktäschel (EMNLP (1), 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 001; Tim Rocktäschel (CoRR, 2020)

Natural Language Inference with a Human Touch: Using Human Explanations to Guide Model Attention. - Joe Stacey; Yonatan Belinkov; Marek Rei (CoRR, 2021)

Supervising Model Attention with Human Explanations for Robust Natural Language Inference. - Joe Stacey; Yonatan Belinkov; Marek Rei (AAAI, 2022)

Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI Models. - Joe Stacey; Pasquale Minervini; Haim Dubossarsky; Marek Rei (EMNLP, 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)

When and Why Does Bias Mitigation Work? - Abhilasha Ravichander; Joe Stacey; Marek Rei (EMNLP (Findings), 2023)

Improving Robustness in Knowledge Distillation Using Domain-Targeted Data Augmentation. - Joe Stacey; Marek Rei (CoRR, 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)

Distilling Robustness into Natural Language Inference Models with Domain-Targeted Augmentation. - Joe Stacey; Marek Rei (ACL (Findings), 2024)

Atomic Inference for NLI with Generated Facts as Atoms. - Joe Stacey; Pasquale Minervini; Haim Dubossarsky; Oana-Maria Camburu; Marek Rei (EMNLP, 2024)

LUCID: LLM-Generated Utterances for Complex and Interesting Dialogues. - Joe Stacey; Jianpeng Cheng 002; John Torr; Tristan Guigue; Joris Driesen; Alexandru Coca; Mark Gaynor; Anders Johannsen (NAACL (Student Research Workshop), 2024)

How to Improve the Robustness of Closed-Source Models on NLI. - Joe Stacey; Lisa Alazraki; Aran Ubhi; Beyza Ermis; Aaron Mueller; Marek Rei (CoRR, 2025)