Papers by Cristina Sarasua

2 papers
DREAM: Deployment of Recombination and Ensembles in Argument Mining (2023.emnlp-main)

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Challenge: Current approaches to Argument Mining (AM) take a holistic view of the overall pipeline.
Approach: They propose a framework that allows for the (automated) combination of AM components instead of all-new solutions.
Outcome: The proposed framework outperforms the best single systems in terms of accuracy measured by an AM benchmark.
HypER: Literature-grounded Hypothesis Generation and Distillation with Provenance (2025.emnlp-main)

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Challenge: Existing approaches focus on retrieval augmentation and focus on the quality of the output . Existing methods focus on generating a highly specific declarative statement ignoring the underlying reasoning process behind ideation.
Approach: They propose a large language model that generates evidence-based hypotheses using literature-guided reasoning and a multi-task setting.
Outcome: The proposed model outperforms the base model and generates evidence-grounded hypotheses with high feasibility and impact as judged by human experts.

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