Papers by Maria Leonor Pacheco
LOGICAL-COMMONSENSEQA: A Benchmark for Logical Commonsense Reasoning (2026.acl-short)
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| Challenge: | LOGICAL-COMMONSENSEQA benchmarks evaluate commonsense reasoning as logical composition over pairs of atomic statements . commonsensible reasoning is central to human cognition and a long-standing challenge in artificial intelligence and natural language understanding. |
| Approach: | They propose a benchmark that reframes commonsense reasoning as logical composition over pairs of atomic statements using plausibility-level operators. |
| Outcome: | LOGICAL-COMMONSENSEQA exposes fundamental reasoning limitations and provides a framework for advancing compositional commonsense reasoning. |
Modeling Human Mental States with an Entity-based Narrative Graph (2021.naacl-main)
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| Challenge: | Understanding narrative text requires capturing characters’ motivations, goals, and mental states. |
| Approach: | They propose an Entity-based Narrative Graph (ENG) to model the internal-states of characters in a story and evaluate it on two narrative understanding tasks. |
| Outcome: | The proposed model is based on two narrative understanding tasks: predicting character mental states, and desire fulfillment. |
Explaining Puzzle Solutions in Natural Language: An Exploratory Study on 6x6 Sudoku (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are effective in human-AI collaborative decision-making, but they lack the ability to provide trustworthy, gradual, and tailored explanations. |
| Approach: | They evaluate the performance of five Large Language Models in solving and explaining Sudoku puzzles. |
| Outcome: | The proposed model can solve and explain complex Sudoku puzzles in a controlled environment. |
A Holistic Framework for Analyzing the COVID-19 Vaccine Debate (2022.naacl-main)
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Maria Leonor Pacheco, Tunazzina Islam, Monal Mahajan, Andrey Shor, Ming Yin, Lyle Ungar, Dan Goldwasser
| Challenge: | Covid-19 infodemic has led to low quality information leading to poor health decisions . authors propose a framework for analyzing false claims and reasoning about the decisions a person makes . |
| Approach: | They propose a framework linking stance and reason analysis and moral sentiment analysis. |
| Outcome: | The proposed framework provides reliable predictions even in low-supervision settings. |
Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks (2022.acl-long)
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| Challenge: | Social media has enabled the propagation of fake news, text published by news sources with an intent to spread misinformation and sway beliefs. |
| Approach: | They propose to use inference operators to analyze social media for fake news spread to uncover unobserved interactions between documents and users' engagement patterns. |
| Outcome: | The proposed algorithms improve the performance of two fake news detection tasks. |
CLIX: Cross-Lingual Explanations of Idiomatic Expressions (2025.findings-acl)
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| Challenge: | Existing definition generation systems are difficult to use in second language learning due to the presence of unfamiliar words and grammar. |
| Approach: | They propose to use cross-lingual explanations of idiomatic expressions to support vocabulary expansion for language learners. |
| Outcome: | The proposed system is able to explain idiomatic expressions in non-standard language. |
Mapping the Course for Prompt-based Structured Prediction (2026.eacl-long)
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| Challenge: | Large language models have demonstrated strong performance in a wide-range of language tasks without task-specific fine-tuning. |
| Approach: | They combine large language models with combinatorial inference to marry predictive power of LLMs with structural consistency provided by inference methods. |
| Outcome: | The proposed model incorporates symbolic inference to provide consistent and accurate predictions on challenging tasks. |
Modeling Content and Context with Deep Relational Learning (2021.tacl-1)
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| Challenge: | Existing frameworks for combining neural and symbolic representations are limited to simple relational learning tasks. |
| Approach: | They propose a declarative framework for specifying deep relational models that integrates expressive language encoders and provides an interface to study the interactions between representation, inference and learning. |
| Outcome: | The proposed framework integrates with expressive language encoders and provides an interface to study the interactions between representation, inference and learning. |
Effects of Collaboration on the Performance of Interactive Theme Discovery Systems (2026.acl-long)
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Alvin Po-Chun Chen, Rohan Das, Dananjay Srinivas, Alexandra Barry, Maksim Seniw, Maria Leonor Pacheco
| Challenge: | NLP-assisted systems to support qualitative data analysis have gained considerable traction, but no unified evaluation framework exists to account for the many different settings in which qualitative researchers may employ them. |
| Approach: | They propose a framework to evaluate the way collaboration settings may produce different research outcomes across a variety of interactive systems. |
| Outcome: | The proposed framework evaluates the impact of synchronous vs. asynchronous collaboration on consistency, cohesiveness, and correctness of qualitative research outcomes. |
Hands-On Interactive Neuro-Symbolic NLP with DRaiL (2022.emnlp-demos)
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| Challenge: | Existing methods to enhance and correct NLP models require feedback from users. |
| Approach: | They propose to enhance DRaiL with an easy to use Python interface that allows users to define, modify and augment models, as well as debug and visualize the predictions. |
| Outcome: | The proposed framework supports predicting sentence and entity level moral sentiment in political tweets. |
Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections (2023.findings-acl)
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| Challenge: | Topic modeling is a popular method for identifying emerging themes from text collections. |
| Approach: | They propose a framework that receives and encodes expert feedback at different levels of abstraction. |
| Outcome: | The proposed framework combines automation and manual coding, allowing experts to maintain control while reducing the manual effort required. |
Lost in Translation, and Found: Detecting and Interpreting Translation Effects (2026.acl-long)
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Shira Wein, Anna Serbina, Jiyuan Ji, Nathan Wolf, Jason DeGraaff, Prajakta Kini, Maria Leonor Pacheco
| Challenge: | Translationese refers to the statistical patterns that distinguish translated texts from original texts. |
| Approach: | They analyze linguistic features which enable our model to achieve high accuracy by a collection of linguistic characteristics and pretrained neural models pick up these features without any fine-tuning. |
| Outcome: | The proposed model achieves high accuracy with a set of linguistic features that correspond to translationese theories and pretrained neural models pick up these features without any fine-tuning. |
Identifying Power Relations in Conversations using Multi-Agent Social Reasoning (2025.naacl-short)
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| Challenge: | Existing approaches to understanding power relationships in conversations are based on task-specific supervised learning. |
| Approach: | They propose a multi-agent social reasoning framework that leverages social science tools to generate and evaluate reasons from multiple perspectives and construct a factor graph for inference. |
| Outcome: | The proposed framework outperforms standard prompting baselines on power dynamics in conversations. |
A Structured Clustering Approach for Inducing Media Narratives (2026.acl-long)
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| Challenge: | Existing approaches to modeling media narratives miss subtle narrative patterns through coarse-grained analysis or require domain-specific taxonomies that limit scalability. |
| Approach: | They propose a framework for inducing rich narrative schemas by jointly modeling events and characters via structured clustering. |
| Outcome: | The proposed framework produces explainable narrative schemas that align with established framing theory while scaling to large corpora without exhaustive manual annotation. |
Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations (2020.findings-emnlp)
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| Challenge: | Structured knowledge representations capture temporal relations between events to describe human-level representations of common scenarios. |
| Approach: | They propose to represent narrative graphs and learn contextualized event representations over them using a relational graph neural network model. |
| Outcome: | The proposed model improves performance when learning script knowledge without supervision and provides a better representation for the implicit discourse sense classification task. |
Randomized Deep Structured Prediction for Discourse-Level Processing (2021.eacl-main)
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| Challenge: | Expressive text encoders have been at the center of recent NLP work . however, some tasks require complex structural dependencies between texts . |
| Approach: | They propose to leverage deep structured prediction and expressive neural encoders for argumentation mining tasks. |
| Outcome: | The proposed framework can be used for argumentation mining tasks without expensive inference tools. |
Identifying Morality Frames in Political Tweets using Relational Learning (2021.emnlp-main)
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| Challenge: | Moral sentiment is often motivated by its targets, which can correspond to individuals or collective entities. |
| Approach: | They propose a model to predict moral attitudes towards entities and moral foundations jointly using tweets written by US politicians. |
| Outcome: | The proposed model predicts moral attitudes towards entities and moral foundations jointly from tweets written by US politicians. |