Project

DEEPLEE

Tiefes Lernen für End-to-End-Anwendungen in der Sprachtechnologie

The research work in DEEPLEE, which is carried out in the Language Technology research departments in Saabrücken and Berlin, builds on DFKI's expertise in the areas of "deep learning" (DL) and "language technology" (LT) and develops it further. They aim for profound improvements of DL approaches in LT by focusing on four central, open research topics:

  1. Modularity in DNN architectures
  2. Use of external knowledge
  3. DNNs with explanation functionality
  4. Machine Teaching Strategies for DNNs

The result of the research work will be a DL-based modular framework system that enables end-to-end applications in information extraction (IE), question answering (QA) and machine translation (MT). The following research objectives are pursued:

  1. Complex LTs (IE, QA, MT), which are traditionally based on heterogeneous technology collections, are to be modeled as uniform end-to-end learning scenarios based on neural networks.
  2. The end-to-end performance of classical approaches based on heterogeneous technology collections is to be evaluated against neural approaches.
  3. A repertoire of "linguistically inspired" neural building blocks for LTs will be established, which are linguistically-agnostic and can be reused (including explanatory functionality and learning aspects such as different degrees of monitoring, model distribution, transfer learning, multi-task learning for such modules). We will do this for IE, QA and MT scenarios covering a wide range of building blocks and applications.
  4. A portfolio of approaches to a variety of DNNs and tasks (NMT, NQA and NIE) will be established, which can be explained to a human expert.
  5. IE, QA and MT are to be designed as text-to-text applications.
  6. Development and evaluation of ways to integrate external knowledge sources into NN-based LTs.

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Contact Person
Prof. Dr. Stephan Busemann

Publications about the project

Ekaterina Lapshinova-Koltunski, Cristina España-Bonet, Josef van Genabith

In: Fourth Workshop on Discourse in Machine Translation. Discourse in Machine Translation (DiscoMT-2019) located at EMNLP-IJCNLP 2019 November 3 Hong Kong China Pages 1-12 ACL 11/2019.

To the publication
Eva Martínez Garcia, Carles Creus, Cristina España-Bonet

In: Fourth Workshop on Discourse in Machine Translation. Discourse in Machine Translation (DiscoMT-2019) located at EMNLP-IJCNLP 2019 November 3-3 Hong Kong China Pages 13-23 ACL 11/2019.

To the publication
Dominik Stammbach, Günter Neumann

In: Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER). Workshop on Fact Extraction and Verification (FEVER-2019) located at EMNLP-IJCNLP 2019 November 3 Hong Kong China Pages 105-109 Association for Computational Linguistics 11/2019.

To the publication

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz