Papers by Erenay Dayanik
DEbateNet-mig15:Tracing the 2015 Immigration Debate in Germany Over Time (2020.lrec-1)
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Gabriella Lapesa, Andre Blessing, Nico Blokker, Erenay Dayanik, Sebastian Haunss, Jonas Kuhn, Sebastian Padó
| Challenge: | a dataset for germany covering the public debate on immigration is annotated . a political science notion of a claim is used to represent the political discourse . |
| Approach: | They annotate a dataset for german public debate on immigration in 2015 using a political science notion of a claim . they identify claims in newspaper articles, assign them to actors and fine-grained categories and annotize their polarity and date. |
| Outcome: | The dataset is annotated by a political science framework and shows it captures political debate . it shows that political actors can change their positions and take a strong stand against them . |
Masking Actor Information Leads to Fairer Political Claims Detection (2020.acl-main)
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| Challenge: | In recent years, NLP methods have found increasing adoption in the social sciences . however, CSS must be crucially interested in the algorithmic fairness of the underlying methods . |
| Approach: | They propose two methods which mask proper names and pronouns during training of the model, thus removing personal information bias. |
| Outcome: | The proposed methods decrease frequency bias while keeping the overall performance stable. |
Improving Neural Political Statement Classification with Class Hierarchical Information (2022.findings-acl)
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Erenay Dayanik, Andre Blessing, Nico Blokker, Sebastian Haunss, Jonas Kuhn, Gabriella Lapesa, Sebastian Pado
| Challenge: | skewed classification of fine-grained categories in text-based computational social science is challenging on the NLP side. |
| Approach: | They propose to use hierarchical relations among categories in codebooks to create constraints on the learned model. |
| Outcome: | The proposed model improves on two datasets and multiple languages. |
HALLUCANA: Fixing LLM Hallucination with A Canary Lookahead (2025.findings-naacl)
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| Challenge: | Existing methods to detect factual hallucinations w.r.t. world knowledge require assistive generations (samples, drafts, etc.) Existing approaches to factuality hallucinism detection, such as SelfCheckGPT, require assistively generation (sequences, Drafts, and etc.). Existing studies on factualism hallucinosation detection require assistives generations (Sample, draft, etc). |
| Approach: | They propose a canary lookahead which detects and corrects factual hallucinations of Large Language Models in long-form generation by exploiting the internal factuality representation in the LLM hidden space. |
| Outcome: | The proposed method improves generation quality by 2.5x while consuming over 6 times less compute. |
Who Sides with Whom? Towards Computational Construction of Discourse Networks for Political Debates (P19-1)
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| Challenge: | a vision of computational construction of discourse networks from newspaper reports is essential for understanding democratic political decision making. |
| Approach: | They propose to use a requirements analysis and an annotated pilot corpus of migration claims to build a computationally-based model of political debates from newspaper reports. |
| Outcome: | The proposed framework could be scaled up to a large scale and be useful for political scientists. |