Papers by Ankita Gupta
ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution (2023.findings-eacl)
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Ankita Gupta, Marzena Karpinska, Wenlong Zhao, Kalpesh Krishna, Jack Merullo, Luke Yeh, Mohit Iyyer, Brendan O’Connor
| Challenge: | Existing datasets vary in definition of coreferences and are curated for linguistic experts. |
| Approach: | They propose to use ezCoref to create a crowdsourcing-friendly coreference annotation methodology that teaches annotators only cases that are treated similarly across existing datasets. |
| Outcome: | The proposed method reannotates 240 passages from seven existing english coreference datasets while teaching annotators only cases that are treated similarly across them. |
Harnessing Toulmin’s theory for zero-shot argument explication (2024.acl-long)
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| Challenge: | To better analyze informal arguments on public forums, we propose the task of argument explication, which makes explicit a text’s argumentative structure and implicit reasoning by outputting triples of propositions claim, reason warrant. |
| Approach: | They propose to prompt generative large language models to output explicit argument components proposed by Toulmin by prompting with the theory name. |
| Outcome: | The proposed method evaluates the outputs’ coverage and validity through a human study and automatic evaluation based on prior argumentation datasets and performs robustness checks over alternative LMs, prompts, and argumentation theories. |
-Stance: A Large-Scale Real World Dataset of Stances in Legal Argumentation (2025.acl-long)
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| Challenge: | Current tools for legal argument reasoning do not support this task. |
| Approach: | They propose to use a large-scale dataset to facilitate work on the legal argument stance classification task by evaluating whether a case summary strengthens or weakens a legal argument. |
| Outcome: | The proposed dataset is used to facilitate work on the legal argument stance classification task, which involves assessing whether a case summary strengthens or weakens a legal argument (polarity) and to what extent (intensity). |
Automated main concept generation for narrative discourse assessment in aphasia (2025.findings-acl)
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| Challenge: | Several advances have been made towards developing theoretical and computational methods for understanding narratives. |
| Approach: | They propose a method that generates MCs from novel stories that experts can edit manually. |
| Outcome: | The proposed method can generate most of the gold standard MCs for stories from an existing narrative summarization dataset. |
Leveraging LLM For Synchronizing Information Across Multilingual Tables (2025.naacl-long)
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| Challenge: | Recent research has sought to improve cross-language synchronization of Wikipedia tables using rule-based methods, but they struggle with complexity and generalization. |
| Approach: | They propose to use a dataset to simulate the process of updating outdated Wikipedia tables and introduce a task decomposition strategy that enhances coherence and accuracy. |
| Outcome: | The proposed model outperforms baselines in Information Updation (1.79%) and Information Addition (20.58%), highlighting its strength in dynamically updating and enriching data across architectures. |
DEMETR: Diagnosing Evaluation Metrics for Translation (2022.emnlp-main)
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| Challenge: | BLEU scores are based on string overlap, but they are opaque in comparison to newer learned metrics. |
| Approach: | They propose a dataset to evaluate MT evaluation metrics based on linguistic perturbations in English . they find learned metrics perform substantially better than string-based metrics . |
| Outcome: | The proposed dataset shows that learned metrics perform better than string-based metrics . the dataset contains 31K English examples that cover 35 different linguistic phenomena . |
How to Fine-Tune Safely on a Budget: Model Adaptation Using Minimal Resources (2025.emnlp-industry)
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Anh C. Pham, Mihir Thalanki, Michael Sun, Aditya Chaloo, Ankita Gupta, Tian Xia, Aditya Mate, Ehi Nosakhare, Soundararajan Srinivasan
| Challenge: | Existing methods for fine-tuning safety examples are underdeveloped. |
| Approach: | They hypothesize that the effectiveness of a safety example is governed by its instruction-response behavior and its semantic diversity across harm categories. |
| Outcome: | The proposed method reduces harmfulness by up to 41% while adding only 0.05% more data to the fine-tuning set. |
NarrativeTime: Dense Temporal Annotation on a Timeline (2024.lrec-main)
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| Challenge: | e.g. TimeBank contains 1-5% of all possible tlinks, and this information is underspecified in the text. |
| Approach: | They propose a timeline-based framework that achieves full coverage of all possible TLINKs. |
| Outcome: | The proposed framework achieves full coverage of all possible TLINKs in a text. |