Search Results for author: Tim Baumgärtner

Found 7 papers, 5 papers with code

UKP-SQuARE v3: A Platform for Multi-Agent QA Research

1 code implementation31 Mar 2023 Haritz Puerto, Tim Baumgärtner, Rachneet Sachdeva, Haishuo Fang, Hao Zhang, Sewin Tariverdian, Kexin Wang, Iryna Gurevych

To ease research in multi-agent models, we extend UKP-SQuARE, an online platform for QA research, to support three families of multi-agent systems: i) agent selection, ii) early-fusion of agents, and iii) late-fusion of agents.

Question Answering

Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking

1 code implementation19 Oct 2022 Tim Baumgärtner, Leonardo F. R. Ribeiro, Nils Reimers, Iryna Gurevych

Pairing a lexical retriever with a neural re-ranking model has set state-of-the-art performance on large-scale information retrieval datasets.

Argument Retrieval Information Retrieval +4

UKP-SQuARE v2: Explainability and Adversarial Attacks for Trustworthy QA

1 code implementation19 Aug 2022 Rachneet Sachdeva, Haritz Puerto, Tim Baumgärtner, Sewin Tariverdian, Hao Zhang, Kexin Wang, Hossain Shaikh Saadi, Leonardo F. R. Ribeiro, Iryna Gurevych

In this paper, we introduce SQuARE v2, the new version of SQuARE, to provide an explainability infrastructure for comparing models based on methods such as saliency maps and graph-based explanations.

Adversarial Attack Explainable Models +2

UKP-SQUARE: An Online Platform for Question Answering Research

1 code implementation ACL 2022 Tim Baumgärtner, Kexin Wang, Rachneet Sachdeva, Max Eichler, Gregor Geigle, Clifton Poth, Hannah Sterz, Haritz Puerto, Leonardo F. R. Ribeiro, Jonas Pfeiffer, Nils Reimers, Gözde Gül Şahin, Iryna Gurevych

Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats (e. g., extractive, abstractive), require different model architectures (e. g., generative, discriminative), and setups (e. g., with or without retrieval).

Explainable Models Information Retrieval +2

The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue

no code implementations ACL 2019 Janosch Haber, Tim Baumgärtner, Ece Takmaz, Lieke Gelderloos, Elia Bruni, Raquel Fernández

This paper introduces the PhotoBook dataset, a large-scale collection of visually-grounded, task-oriented dialogues in English designed to investigate shared dialogue history accumulating during conversation.

On the Realization of Compositionality in Neural Networks

no code implementations WS 2019 Joris Baan, Jana Leible, Mitja Nikolaus, David Rau, Dennis Ulmer, Tim Baumgärtner, Dieuwke Hupkes, Elia Bruni

We present a detailed comparison of two types of sequence to sequence models trained to conduct a compositional task.

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