Papers by Cennet Oguz
Chop and Change: Anaphora Resolution in Instructional Cooking Videos (2022.findings-aacl)
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| Challenge: | temporally evolving entities present challenges for anaphora resolution tasks . recipes provide rich source for referring expressions of transformed entities . |
| Approach: | They propose to use annotations to annotate recipes for anaphora resolution task . they propose to employ temporal features to improve anamorphic resolution . |
| Outcome: | The proposed annotation scheme improves the performance of the anaphora resolution task. |
MMAR: Multilingual and Multimodal Anaphora Resolution in Instructional Videos (2024.findings-emnlp)
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| Challenge: | Existing approaches to multilingual anaphora resolution include images and video inputs. |
| Approach: | They propose to include multimodal information in the form of images in anaphora resolution tasks. |
| Outcome: | The proposed approach improves resolution by 10% for unseen languages. |
Find-2-Find: Multitask Learning for Anaphora Resolution and Object Localization (2023.emnlp-main)
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| Challenge: | Existing systems require large number of accurate annotations, such as image-level labels and location-level labeling. |
| Approach: | They propose a joint anaphora resolution and object localization dataset targeting visual-linguistic ambiguity. |
| Outcome: | The proposed framework improves visual-linguistic alignment and object localization with one joint model compared to a strong single-task baseline. |
Few-shot Learning for Slot Tagging with Attentive Relational Network (2021.eacl-main)
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| Challenge: | Recent studies have used metric-based learning in computer vision but not slot tagging. |
| Approach: | They propose a metric-based learning architecture that extends relation networks by leveraging pretrained contextual embeddings such as ELMO and BERT and by using attention mechanism. |
| Outcome: | The proposed method outperforms state-of-the-art methods on SNIPS data on a slot tagging task with a large amount of hand-labeled data. |
DualFact+: A Multimodal Fact Verification Framework for Procedural Video Captioning (2026.findings-acl)
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| Challenge: | Existing evaluation metrics fail to evaluate factual correctness in procedural video captions . Existing metrics rely on lexical overlap or holistic semantic similarity, but miss role-specific omissions resulting in hallucinations . |
| Approach: | They propose a role-aware, fact-level evaluation framework that distinguishes conceptual facts from contextual facts. |
| Outcome: | Experiments show that state-of-the-art captioning models produce fluent but incomplete descriptions with systematic errors. |
InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations (2023.findings-emnlp)
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| Challenge: | Recent work on NLP explainability methods lacks a dialogue-based interpretability framework that can convey faithful explanations in human-understandable terms. |
| Approach: | They adapt the conversational explanation framework TalkToModel to the NLP domain and add new NLP-specific operations such as free-text rationalization to illustrate its generalizability. |
| Outcome: | The proposed framework can be used to explain models on three NLP tasks and is generalizable to different datasets, use cases and models. |