Papers by Tim Baumgärtner
Ask No More: Deciding when to guess in referential visual dialogue (C18-1)
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| Challenge: | Using a task-oriented visual dialogue model, we add a decision-making component that decides whether to ask a follow-up question to identify a target referent in an image, or to stop the conversation to make a guess. |
| Approach: | They augment a task-oriented visual dialogue model with a decision-making component that decides whether to ask a follow-up question to identify a target referent in an image, or to stop the conversation to make a guess. |
| Outcome: | The proposed model can be enhanced with a decision-making component that decides whether to ask a follow-up question to identify a target referent in an image, or to stop the conversation to make a guess. |
Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking (2022.emnlp-main)
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| Challenge: | Extensive experiments demonstrate that integrating relevance feedback directly into neural re-ranking models improves their performance. |
| Approach: | They propose a kNN approach that re-ranks documents based on their similarity with the query and the documents the user considers relevant. |
| Outcome: | The proposed approach outperforms all other methods by 5.2% on large-scale information retrieval datasets. |
Scalable and Domain-General Abstractive Proposition Segmentation (2024.findings-emnlp)
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| Challenge: | Several recent studies have demonstrated the utility of proposition segmentation for downstream tasks. |
| Approach: | They propose a scalable, yet accurate, proposition segmentation model that can be supervised by LLMs. |
| Outcome: | The proposed model improves on training on annotated datasets and shows that it is easy to use. |
PeerQA: A Scientific Question Answering Dataset from Peer Reviews (2025.naacl-long)
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| Challenge: | a dataset of 579 QA pairs from 208 scientific articles contains answers that reviewers raised while thoroughly examining the scientific article. |
| Approach: | They propose a dataset that contains questions that reviewers raised while thoroughly examining the scientific article. |
| Outcome: | The proposed dataset contains 579 QA pairs from 208 academic articles . the results show that decontextualization approaches improve retrieval performance . |
The Good, the Bad and the Constructive: Automatically Measuring Peer Review’s Utility for Authors (2025.emnlp-main)
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| Challenge: | Providing constructive feedback to authors is a core component of peer review . authors lack guidance on how to improve their review, a problem that is often overlooked . |
| Approach: | They use a RevUtil dataset to benchmark fine-tuned models for assessing review comments . they find that machine-generated reviews generally underperform human reviews on these aspects . |
| Outcome: | The proposed model outperforms closed models on four aspects of review comments . the proposed model achieves agreement levels comparable to and exceeding those of human models . |
Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat (N19-1)
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Ravi Shekhar, Aashish Venkatesh, Tim Baumgärtner, Elia Bruni, Barbara Plank, Raffaella Bernardi, Raquel Fernández
| Challenge: | Existing systems that address the abilities that need to be put to work during conversations are lacking in terms of visual grounding. |
| Approach: | They propose a visually-grounded dialogue state encoder which integrates visual grounding with dialogue system components. |
| Outcome: | The proposed system improves the GuessWhat?! game by combining guessing and asking questions with multi-task learning. |
UKP-SQuARE v2: Explainability and Adversarial Attacks for Trustworthy QA (2022.aacl-demo)
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Rachneet Sachdeva, Haritz Puerto, Tim Baumgärtner, Sewin Tariverdian, Hao Zhang, Kexin Wang, Hossain Shaikh Saadi, Leonardo F. R. Ribeiro, Iryna Gurevych
| Challenge: | Question Answering (QA) systems rely on deep neural networks, which are difficult to interpret by humans. |
| Approach: | They propose an interpretable model that provides an explanation infrastructure for comparing models based on saliency maps and graph-based explanations. |
| Outcome: | The proposed methods can be used to compare models based on saliency maps and graph-based explanations. |
SciCoQA: Quality Assurance for Scientific Paper–Code Alignment (2026.acl-long)
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| Challenge: | Discrepancies between scientific papers and their code undermine reproducibility, a concern that grows as automated research agents scale scientific output beyond human review capacity. |
| Approach: | They propose a synthetic generation pipeline to scale beyond AI to Physics, Quantitative Biology, and other computational sciences. |
| Outcome: | The proposed pipeline scales beyond AI to Physics, Quantitative Biology, and other computational sciences. |
The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue (P19-1)
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| Challenge: | Using the PhotoBook dataset, we investigate shared dialogue history accumulating during conversation . human interlocutors are known to collaboratively establish a shared repository of mutual information during a conversation - this common ground is then used to optimise understanding and communication efficiency. |
| Approach: | They propose a data-collection task formulated as a collaborative game prompting two online participants to refer to images utilising both their visual context and previously established referring expressions. |
| Outcome: | The proposed model takes into account shared information accumulated in a reference chain and is important to resolve later descriptions. |
UKP-SQUARE: An Online Platform for Question Answering Research (2022.acl-demo)
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Tim Baumgärtner, Kexin Wang, Rachneet Sachdeva, Gregor Geigle, Max Eichler, Clifton Poth, Hannah Sterz, Haritz Puerto, Leonardo F. R. Ribeiro, Jonas Pfeiffer, Nils Reimers, Gözde Şahin, Iryna Gurevych
| Challenge: | Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats and require different model architectures and setups. |
| Approach: | They propose an extensible online QA platform that allows users to query and analyze a large collection of modern Skills via a user-friendly web interface and integrated behavioural tests. |
| Outcome: | The proposed tool allows users to query and analyze a large collection of modern Skills via a user-friendly web interface and integrated behavioural tests. |
UKP-SQuARE v3: A Platform for Multi-Agent QA Research (2023.acl-demo)
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Haritz Puerto, Tim Baumgärtner, Rachneet Sachdeva, Haishuo Fang, Hao Zhang, Sewin Tariverdian, Kexin Wang, Iryna Gurevych
| Challenge: | Current approaches to QA models are multi-dataset models, but combining expert agents can yield large performance gains over multi-agent models. |
| Approach: | They extend an online platform for QA research to support three families of multi-agent systems: agent selection, early-fusion of agents, and late-fusion. |
| Outcome: | The proposed model can be compared with multi-dataset models and achieve high inference speed and performance. |