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:

3SC – Semantic Search for Science Communication

Launched in 2019, the joint project of Informationsdienst Wissenschaft (idw) and DFKI uses the Generalized Semantics Retrieval Engine (GSRE) of the QAIE research group for semantic search on the distribution service for scientific press releases in Germany.

Project Page


Period: 01.05.2018–30.04.2022
Personalised Medicine by Predictive Modelling in Stroke for better Quality of Life, funded by EU.

Project Page


Period: 01.01.2017–31.12.2020
Personalised Reading Apps for Primary School Children, funded by European Union (H2020, No 731724).

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


Co(n)textual Reasoning and Adaptation for Natural Language Processing

Period: 01.10.2020-30.09.2023, funded by BMBF.

Project website


XAINES (01.09.2020 - 31.08.2024): Explaining AI with Narratives

Funded by BMBF (01IW20005).

Project website

Selected recent publications:

  • Saadullah Amin, Günter Neumann (2021) T2NER: Transformers based transfer learning framework for named entity recognition, In Proceedings of EACL, 2021.
  • Saadullah Amin, Stalin Varanasi, Katherine Dunfield, Günter Neumann (2020) LowFER: Low-rank Bilinear Pooling for Link Prediction. In: Proceedings of the 37th International Conference on Machine Learning (ICML-2020), Online, PMLR 119, 2020.
  • Saadullah Amin, Katherine Dunfield, Anna Vechkaeva, Günter Neumann (2020) A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction. In: BioNLP 2020 Workshop on Biomedical Natural Language Processing. Workshop on Current Trends in Biomedical Natural Language Processing (BioNLP-2020) located at The 58th Annual Meeting of the Association for Computational Linguistics July 9-9 ACL 2020.
  • Stalin Varanasi, Saadullah Amin, Günter Neumann (2020) CopyBERT: A Unified Approach to Question Generation with Self-Attention. In: NLP for Conversational AI - Proceedings of the 2nd Workshop. NLP for Conversational AI (NLPConvAI-2020) July 9-9 Pages 25-31 ISBN 978-1-952148-08-8 ACL 2020.
  • Alejandro Figueroa, Carlos Gómez-Pantoja, Günter Neumann (2019) Integrating heterogeneous sources for predicting question temporal anchors across Yahoo! Answers. In: Information Fusion 50 Pages 112-125 Elsevier 10/2019.
  • Saadullah Amin, Günter Neumann, Katherine Dunfield, Anna Vechkaeva, Kathryn Annette Chapman, Morgan Kelly Wixted (2019) MLT-DFKI at CLEF eHealth 2019: Multi-label Classification of ICD-10 Codes with BERT. In: CLEF 2019 Working Notes. Conference and Labs of the Evaluation Forum (CLEF-2019) 10th Conference and Labs of the Evaluation Forum September 9-12 Lugano Switzerland 9/2019.

QAIE Members

Team Lead:

Prof. Dr. Günter Neumann

Team Members:

Saadullah Amin
Günter Neumann
Jörg Steffen
Stalin Varanasi

Research Assistants:

Ekaterina Arkhangelskaia
Kathryn Annette Chapman
Ioannis Dikeoulias
Noon Pokaratsiri

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz