Search Results for author: Alexis Ross

Found 11 papers, 6 papers with code

ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews

1 code implementation21 Jun 2023 Mike D'Arcy, Alexis Ross, Erin Bransom, Bailey Kuehl, Jonathan Bragg, Tom Hope, Doug Downey

Revising scientific papers based on peer feedback is a challenging task that requires not only deep scientific knowledge and reasoning, but also the ability to recognize the implicit requests in high-level feedback and to choose the best of many possible ways to update the manuscript in response.

CREST: A Joint Framework for Rationalization and Counterfactual Text Generation

1 code implementation26 May 2023 Marcos Treviso, Alexis Ross, Nuno M. Guerreiro, André F. T. Martins

Selective rationales and counterfactual examples have emerged as two effective, complementary classes of interpretability methods for analyzing and training NLP models.

counterfactual Data Augmentation +2

Does Self-Rationalization Improve Robustness to Spurious Correlations?

no code implementations24 Oct 2022 Alexis Ross, Matthew E. Peters, Ana Marasović

Specifically, we evaluate how training self-rationalization models with free-text rationales affects robustness to spurious correlations in fine-tuned encoder-decoder and decoder-only models of six different sizes.

Competency Problems: On Finding and Removing Artifacts in Language Data

no code implementations EMNLP 2021 Matt Gardner, William Merrill, Jesse Dodge, Matthew E. Peters, Alexis Ross, Sameer Singh, Noah A. Smith

In this work we argue that for complex language understanding tasks, all simple feature correlations are spurious, and we formalize this notion into a class of problems which we call competency problems.


Explaining NLP Models via Minimal Contrastive Editing (MiCE)

1 code implementation Findings (ACL) 2021 Alexis Ross, Ana Marasović, Matthew E. Peters

Humans have been shown to give contrastive explanations, which explain why an observed event happened rather than some other counterfactual event (the contrast case).

counterfactual Multiple-choice +4

Learning Models for Actionable Recourse

1 code implementation NeurIPS 2021 Alexis Ross, Himabindu Lakkaraju, Osbert Bastani

As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for generating counterfactual explanations.

counterfactual Decision Making

How well do NLI models capture verb veridicality?

no code implementations IJCNLP 2019 Alexis Ross, Ellie Pavlick

In natural language inference (NLI), contexts are considered veridical if they allow us to infer that their underlying propositions make true claims about the real world.

Natural Language Inference Negation +1

Probing What Different NLP Tasks Teach Machines about Function Word Comprehension

no code implementations SEMEVAL 2019 Najoung Kim, Roma Patel, Adam Poliak, Alex Wang, Patrick Xia, R. Thomas McCoy, Ian Tenney, Alexis Ross, Tal Linzen, Benjamin Van Durme, Samuel R. Bowman, Ellie Pavlick

Our results show that pretraining on language modeling performs the best on average across our probing tasks, supporting its widespread use for pretraining state-of-the-art NLP models, and CCG supertagging and NLI pretraining perform comparably.

CCG Supertagging Language Modelling +3

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