Papers by Mahnaz Koupaee
Modeling Preconditions in Text with a Crowd-sourced Dataset (2020.findings-emnlp)
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Heeyoung Kwon, Mahnaz Koupaee, Pratyush Singh, Gargi Sawhney, Anmol Shukla, Keerthi Kumar Kallur, Nathanael Chambers, Niranjan Balasubramanian
| Challenge: | Existing methods for modeling preconditions in text are limited due to the lack of large scale labeled data grounded in text. |
| Approach: | They propose a crowd-sourced annotation of preconditions between event pairs in newswire that is larger than prior annotations. |
| Outcome: | The proposed model outperforms existing models on two task sets, showing that precondition knowledge is not easily accessible in LM-derived representations alone. |
PASTA: A Dataset for Modeling PArticipant STAtes in Narratives (2023.tacl-1)
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Sayontan Ghosh, Mahnaz Koupaee, Isabella Chen, Francis Ferraro, Nathanael Chambers, Niranjan Balasubramanian
| Challenge: | Existing models that understand narratives should infer these implicit states and their causal relationships with the narrative's explicit events. |
| Approach: | They propose a dataset that contains inferable participant states, a counterfactual perturbation to each state and the changes to the story that would be necessary if the counterfact was true. |
| Outcome: | The proposed model can reason about the impact of changes to the story that would be necessary if the counterfactual were true. |
Don’t Let Discourse Confine Your Model: Sequence Perturbations for Improved Event Language Models (2021.acl-short)
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| Challenge: | Existing approaches to train event language models on text constrain them to follow discourse order of events. |
| Approach: | They propose a method to perturb event sequences so that they can relax model dependence on text order. |
| Outcome: | The proposed technique improves performance on applications and out-of-domain events data. |
SAGEViz: SchemA GEneration and Visualization (2023.emnlp-demo)
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Sugam Devare, Mahnaz Koupaee, Gautham Gunapati, Sayontan Ghosh, Sai Vallurupalli, Yash Kumar Lal, Francis Ferraro, Nathanael Chambers, Greg Durrett, Raymond Mooney, Katrin Erk, Niranjan Balasubramanian
| Challenge: | Schema induction involves creating a graph representation depicting how events unfold . supervised and few-shot approaches are not scalable and time-consuming . |
| Approach: | They propose a tool that utilizes human-AI collaboration to create and update complex schema graphs efficiently. |
| Outcome: | The proposed tool can generate schemas of better quality and be used by users in a variety of domains. |
Faithful, Unfaithful or Ambiguous? Multi-Agent Debate with Initial Stance for Summary Evaluation (2025.naacl-long)
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Mahnaz Koupaee, Jake W. Vincent, Saab Mansour, Igor Shalyminov, Han He, Hwanjun Song, Raphael Shu, Jianfeng He, Yi Nian, Amy Wing-mei Wong, Kyu J. Han, Hang Su
| Challenge: | Existing approaches to evaluate faithfulness of summaries are often fooled by the fluency of the text and struggle with identifying errors. |
| Approach: | They propose an approach to summary faithfulness evaluation where multiple LLM-based agents are assigned initial stances and forced to come up with a reason to justify belief. |
| Outcome: | The proposed approach can identify ambiguities and have even stronger performance on non-ambiguous summaries. |
Modeling Complex Event Scenarios via Simple Entity-focused Questions (2023.eacl-main)
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| Challenge: | Event schemas describe a sequence of events in a particular context, but they are difficult to model with standard event language models. |
| Approach: | They propose a question-guided generation framework that generates events as answers to questions about participants. |
| Outcome: | The proposed framework provides better coverage of participants, diverse events within a domain, comparable perplexities for modeling event sequences, and more effective control for interactive schema generation. |
Author’s Sentiment Prediction (2020.coling-main)
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| Challenge: | Existing work on inferring author sentiment in news articles hasn't been done on this domain. |
| Approach: | They propose a crowd-sourced dataset that captures the sentiment of an author towards the main entity in a news article. |
| Outcome: | The proposed dataset performs the best amongst the baselines, but only achieves modest performance overall suggesting that fine-tuning document-level representations aloneisn’t adequate for this task. |
Causal Graph based Event Reasoning using Semantic Relation Experts (2025.acl-long)
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| Challenge: | Recent advances in event reasoning have limited ability to accurately identify causal connections between events. |
| Approach: | They propose a collaborative approach to generate correct graphs and graphs to assist reasoning . they propose 'a causal chain of events' task that requires a causal link between events . |
| Outcome: | The proposed approach achieves competitive results with state-of-the-art models on forecasting and next event prediction tasks. |