Lucas Emanuel Resck

2024

1. NAACL
   Findings 2024

   Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales

   Lucas E. Resck, Marcos M. Raimundo, and Jorge Poco

   In Findings of the Association for Computational Linguistics: NAACL 2024, Jun 2024

   To appear

   Abs arXiv Bib PDF Code

   Saliency post-hoc explainability methods are important tools for understanding increasingly complex NLP models. While these methods can reflect the model’s reasoning, they may not align with human intuition, making the explanations not plausible. In this work, we present a methodology for incorporating rationales, which are text annotations explaining human decisions, into text classification models. This incorporation enhances the plausibility of post-hoc explanations while preserving their faithfulness. Our approach is agnostic to model architectures and explainability methods. We introduce the rationales during model training by augmenting the standard cross-entropy loss with a novel loss function inspired by contrastive learning. By leveraging a multi-objective optimization algorithm, we explore the trade-off between the two loss functions and generate a Pareto-optimal frontier of models that balance performance and plausibility. Through extensive experiments involving diverse models, datasets, and explainability methods, we demonstrate that our approach significantly enhances the quality of model explanations without causing substantial (sometimes negligible) degradation in the original model’s performance.

   @inproceedings{resck_exploring_2024,
     address = {Mexico City, Mexico},
     title = {Exploring the {Trade}-off {Between} {Model} {Performance} and {Explanation} {Plausibility} of {Text} {Classifiers} {Using} {Human} {Rationales}},
     copyright = {Copyright © 2024 Association for Computational Linguists (ACL). Licensed under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). Disclaimer: No warranties are given. The ACL shall not be liable for any damages arising from the use of this article.},
     url = {https://arxiv.org/abs/2404.03098},
     urldate = {2024-04-05},
     booktitle = {Findings of the {Association} for {Computational} {Linguistics}: {NAACL} 2024},
     publisher = {Association for Computational Linguistics},
     author = {Resck, Lucas E. and Raimundo, Marcos M. and Poco, Jorge},
     month = jun,
     year = {2024},
     note = {To appear},
     bibtex_show = true,
   }

2023

1. AISTATS 2023

   Distill n’ Explain: explaining graph neural networks using simple surrogates

   Tamara Pereira, Erik Nascimento, Lucas E. Resck, Diego Mesquita, and Amauri Souza

   In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, Apr 2023

   Abs arXiv Bib HTML PDF Code Video Poster

   Explaining node predictions in graph neural networks (GNNs) often boils down to finding graph substructures that preserve predictions. Finding these structures usually implies back-propagating through the GNN, bonding the complexity (e.g., number of layers) of the GNN to the cost of explaining it. This naturally begs the question: Can we break this bond by explaining a simpler surrogate GNN? To answer the question, we propose Distill n’ Explain (DnX). First, DnX learns a surrogate GNN via knowledge distillation. Then, DnX extracts node or edge-level explanations by solving a simple convex program. We also propose FastDnX, a faster version of DnX that leverages the linear decomposition of our surrogate model. Experiments show that DnX and FastDnX often outperform state-of-the-art GNN explainers while being orders of magnitude faster. Additionally, we support our empirical findings with theoretical results linking the quality of the surrogate model (i.e., distillation error) to the faithfulness of explanations.

   @inproceedings{pereira_distill_2023-1,
     address = {Valencia, Spain},
     series = {Proceedings of {Machine} {Learning} {Research}},
     title = {Distill n' {Explain}: explaining graph neural networks using simple surrogates},
     volume = {206},
     shorttitle = {Distill n' {Explain}},
     url = {https://proceedings.mlr.press/v206/pereira23a.html},
     language = {en},
     urldate = {2023-04-30},
     booktitle = {Proceedings of {The} 26th {International} {Conference} on {Artificial} {Intelligence} and {Statistics}},
     publisher = {PMLR},
     author = {Pereira, Tamara and Nascimento, Erik and Resck, Lucas E. and Mesquita, Diego and Souza, Amauri},
     month = apr,
     year = {2023},
     pages = {6199--6214},
     bibtex_show = true,
   }

2. TVCG 2023
   VIS 2022

   LegalVis: Exploring and Inferring Precedent Citations in Legal Documents

   Lucas E. Resck, Jean R. Ponciano, Luis Gustavo Nonato, and Jorge Poco

   IEEE Transactions on Visualization and Computer Graphics, Jun 2023

   Presented at Visualization & Visual Analytics (VIS) 2022

   Abs arXiv Bib HTML PDF Video

   To reduce the number of pending cases and conflicting rulings in the Brazilian Judiciary, the National Congress amended the Constitution, allowing the Brazilian Supreme Court (STF) to create binding precedents (BPs), i.e., a set of understandings that both Executive and lower Judiciary branches must follow. The STF’s justices frequently cite the 58 existing BPs in their decisions, and it is of primary relevance that judicial experts could identify and analyze such citations. To assist in this problem, we propose LegalVis, a web-based visual analytics system designed to support the analysis of legal documents that cite or could potentially cite a BP. We model the problem of identifying potential citations (i.e., non-explicit) as a classification problem. However, a simple score is not enough to explain the results; that is why we use an interpretability machine learning method to explain the reason behind each identified citation. For a compelling visual exploration of documents and BPs, LegalVis comprises three interactive visual components: the first presents an overview of the data showing temporal patterns, the second allows filtering and grouping relevant documents by topic, and the last one shows a document’s text aiming to interpret the model’s output by pointing out which paragraphs are likely to mention the BP, even if not explicitly specified. We evaluated our identification model and obtained an accuracy of 96%; we also made a quantitative and qualitative analysis of the results. The usefulness and effectiveness of LegalVis were evaluated through two usage scenarios and feedback from six domain experts.

   @article{resck_legalvis_2023,
     title = {{LegalVis}: {Exploring} and {Inferring} {Precedent} {Citations} in {Legal} {Documents}},
     volume = {29},
     issn = {1941-0506},
     shorttitle = {{LegalVis}},
     url = {https://doi.org/10.1109/TVCG.2022.3152450},
     doi = {10.1109/TVCG.2022.3152450},
     language = {English},
     number = {6},
     urldate = {2023-05-15},
     journal = {IEEE Transactions on Visualization and Computer Graphics},
     author = {Resck, Lucas E. and Ponciano, Jean R. and Nonato, Luis Gustavo and Poco, Jorge},
     month = jun,
     year = {2023},
     note = {Presented at Visualization \& Visual Analytics (<b>VIS</b>) 2022},
     keywords = {Data visualization, Task analysis, Visual analytics, Law, Natural language processing, Legal documents, natural language processing, Analytical models, Text analysis, Brazilian legal system, visual analytics},
     pages = {3105--3120},
     bibtex_show = true,
   }