ESG Accountability Made Easy: DocQA at Your Service

Authors

  • Lokesh Mishra IBM Research, Rüschlikon, Switzerland
  • Cesar Berrospi IBM Research, Rüschlikon, Switzerland
  • Kasper Dinkla IBM Research, Rüschlikon, Switzerland
  • Diego Antognini IBM Research, Rüschlikon, Switzerland
  • Francesco Fusco IBM Research, Rüschlikon, Switzerland
  • Benedikt Bothur IBM Technology, Zürich, Switzerland
  • Maksym Lysak IBM Research, Rüschlikon, Switzerland
  • Nikolaos Livathinos IBM Research, Rüschlikon, Switzerland
  • Ahmed Nassar IBM Research, Rüschlikon, Switzerland
  • Panagiotis Vagenas IBM Research, Rüschlikon, Switzerland
  • Lucas Morin IBM Research, Rüschlikon, Switzerland ETH Zürich, Zürich, Switzerland
  • Christoph Auer IBM Research, Rüschlikon, Switzerland
  • Michele Dolfi IBM Research, Rüschlikon, Switzerland
  • Peter Staar IBM Research, Rüschlikon, Switzerland

DOI:

https://doi.org/10.1609/aaai.v38i21.30574

Keywords:

Artificial Intelligence, Natural language processing and speech recognition, Systems that integrate different AI technologies

Abstract

We present Deep Search DocQA. This application enables information extraction from documents via a question-answering conversational assistant. The system integrates several technologies from different AI disciplines consisting of document conversion to machine-readable format (via computer vision), finding relevant data (via natural language processing), and formulating an eloquent response (via large language models). Users can explore over 10,000 Environmental, Social, and Governance (ESG) disclosure reports from over 2000 corporations. The Deep Search platform can be accessed at: https://ds4sd.github.io.
AAAI-24 / IAAI-24 / EAAI-24 Proceedings Cover

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Published

2024-03-24

How to Cite

Mishra, L., Berrospi, C., Dinkla, K., Antognini, D., Fusco, F., Bothur, B., Lysak, M., Livathinos, N., Nassar, A., Vagenas, P., Morin, L., Auer, C., Dolfi, M., & Staar, P. (2024). ESG Accountability Made Easy: DocQA at Your Service. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23814-23816. https://doi.org/10.1609/aaai.v38i21.30574

Issue

Vol. 38 No. 21: IAAI-24, EAAI-24, AAAI-24 Student Abstracts, Undergraduate Consortium and Demonstrations

Section

AAAI Demonstration Track