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Multilinguality and Language Technology


We are a team at the MLT-Lab at DFKI specializing on Question Answering (QA) and Information Extraction (IE) – simply QAIE.

Question Answering (finding and extracting answers for natural language questions from text) and Information Extraction (extracting entities and relations from text) are closely related: both share a number of sub-tasks like named entity recognition, relation extraction and entity linking. We research and develop methods combining Language Technology, Machine Learning and Deep Learning from core components to complete end-to-end solutions. We develop QAIE solutions in many application areas, including e-Health, BioNLP, e-Learning, and intelligent assistance.

Demo: Automatic Question Generation with Deep Learning

Some current projects:


Eliciting and Exploiting Procedural Knowledge in Industry 5.0.

Project Page

Meere Online

Meere Online is a project of the German Marine Research Alliance in the core area of transfer. Scientific facts on relevant marine topics are compiled on the digital information portal Meere Online. As a highlight, an AI-supported search will make it easier for users to access the topics.

Project Page

Fair Forward

Period: 01.05.2020–30.06.2021
Consulting services to Gesellschaft für Internationale Zusammenarbeit (GIZ) on technical aspects of AI in international cooperation including natural language processing (NLP), training data and data access for FAIR Forward – Artificial Intelligence for All. GIZ Project No. 19.2010.7-003.00

Project Page


XAINES (01.09.2020 - 31.08.2024): Explaining AI with Narratives

Funded by BMBF (01IW20005).

Project website

Selected recent publications:

  • Stalin Varanasi, Muhammad Umer Butt, and Günter Neumann (2023) AutoQIR: Auto-Encoding Questions with Retrieval Augmented Decoding for Unsupervised Passage Retrieval and Zero-shot Question Generation, Proceedings of Recent Advances in Natural Language Processing (RANLP-2023), Bulgaria, 2023.
  • Saadullah Amin, Pasquale Minervini, David Chang, Pontus Stenetorp, and Günter Neumann (2022) MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction, Proceedings of The 29th International Conference on Computational Linguistics (Coling-2022), October 12-17, 2022, Gyeongju, Republic of Korea
  • Ioannis Dikeoulias, Saadullah Amin, and Günter Neumann (2022) Temporal Knowledge Graph Reasoning with Low-rank and Model-agnostic Representations , Proceedings of the 7th Workshop on Representation Learning for NLP. ACL-2022, RepL4NLP May 2022, Pages 111-120 ACL 5/2022 (RepL4NLP-2022), May, 2022.
  • Saadullah Amin, Noon Pokaratsiri, Morgan Wixted, Alejandro García-Rudolph, Catalina Martínez-Costa, and Günter Neumann (2022) Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts , Proceedings of the 21st Workshop on Biomedical Language Processing. ACL-2022 BioNLP, May 22-27, Pages 200-211 ACL 5/2022. (BioNLP-2022), May, 2022.
  • Stalin Varanasi, Saadullah Amin and Günter Neumann (2021) AutoEQA: Auto-Encoding Questions for Extractive Question Answering, In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2021), Nov. 2021.