Papers by Victoria Lin
Counterfactual Augmentation for Multimodal Learning Under Presentation Bias (2023.findings-emnlp)
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| Challenge: | In real-world machine learning systems, labels are often derived from user behaviors that the system wishes to encourage. |
| Approach: | They propose a method for correcting presentation bias using generated counterfactual labels by augmentation of the labels by the user. |
| Outcome: | The proposed method improves performance in an oracle setting compared to uncorrected models and existing bias-correction methods. |
SenteCon: Leveraging Lexicons to Learn Human-Interpretable Language Representations (2023.findings-acl)
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| Challenge: | In many settings, it is important to understand a model’s decision-making process. |
| Approach: | They propose a method for introducing human interpretability in deep language representations by encoding a passage of text as a layer of interpretable categories. |
| Outcome: | The proposed method outperforms existing interpretable language representations on downstream tasks and on agreement with human characterizations of the text. |
Text-Transport: Toward Learning Causal Effects of Natural Language (2023.emnlp-main)
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| Challenge: | Existing methods for causal inference require strong assumptions about the data, meaning the data from which one *can* estimate valid causal effects is not representative of the actual target domain of interest. |
| Approach: | They propose a method for estimation of causal effects from natural language under any text distribution using the notion of distribution shift. |
| Outcome: | The proposed method can be used to estimate causal effects from natural language under any text distribution. |
Evaluating Models’ Local Decision Boundaries via Contrast Sets (2020.findings-emnlp)
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Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, Ben Zhou
| Challenge: | Standard test sets for supervised learning evaluate in-distribution generalization but are misleading when a dataset has systematic gaps. |
| Approach: | They propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data. |
| Outcome: | The proposed model performs significantly lower on contrast sets than on the original test sets—up to 25% in some cases. |