Skip to main content Skip to main navigation

Project

IMPRESS

Improving Embeddings with Semantic Knowledge

Improving Embeddings with Semantic Knowledge

  • Duration:
  • Application fields
    Other

Virtually all NLP systems nowadays use vector representations of words, a.k.a. word embeddings. Similarly, the processing of language combined with vision or other sensory modalities employs multimodal embeddings. While embeddings do embody some form of semantic relatedness, the exact nature of the latter remains unclear. This loss of precise semantic information can affect downstream tasks.

The goals of IMPRESS are to investigate the integration of semantic and common sense knowledge into linguistic and multimodal embeddings and the impact on selected downstream tasks. IMPRESS will also develop open source software and lexical resources, focusing on video activity recognition as a practical testbed. Furthermore, while there is a growing body of NLP research on languages other than English, most research on multimodal embeddings is still done on English. IMPRESS will consider a multilingual extension of the developed methods to handle French, German and English.

Partners

  1. DFKI 2. INRIA

Sponsors

BMBF - Federal Ministry of Education and Research

01IS20076

BMBF - Federal Ministry of Education and Research

Publications about the project

Stalin Varanasi; Muhammad Umer Butt; Gunter Neumann

In: Large Language Models for Natural Language Processing. International Conference on Recent Advances in Natural Language Processing (RANLP-2023), located at RANLP, September 4-6, Varna, Bulgaria, Pages 1171-1179, ISBN ISBN 978-954-452-092-2, INCOMA Ltd. Shoumen, BULGARIA, 9/2023.

To the publication

Tatiana Anikina; Natalia Skachkova; Joseph Renner; Priyansh Trivedi

In: Juntao Yu; Sopan Khosla; Ramesh Manuvinakurike; Lori Levin; Vincent Ng; Massimo Poesio; Michael Strube; Carolyn Rose (Hrsg.). Proceedings of the CODI-CRAC 2022 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue. Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC-2022), located at COLING, October 16-17, Gyeongju, Korea, Republic of, Pages 15-27, Association for Computational Linguistics, 10/2022.

To the publication

Natalia Skachkova; Tatiana Anikina; Cennet Oguz; Ivana Kruijff-Korbayová; Sharmila Upadhyaya; Siyu Tao

In: Sopan Khosla; Ramesh Manuvinakurike; Vincent Ng; Massimo Poesio; Michael Strube; Carolyn Rosé (Hrsg.). Proceedings of the CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue. Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC-2021), located at EMNLP 2021, November 10-11, Punta Cana, Dominican Republic, Pages 63-70, Association for Computational Linguistics, 11/2021.

To the publication