After the successful launch of the Economics and Natural Language Processing (ECONLP) workshop at ACL 2018 in Melbourne, Australia and the follow-up event at EMNLP-IJCNLP 2019 in Hong Kong, China, and EMNLP 2021 in Punta Cana, Dominican Republic, we will run the fourth edition of ECONLP at LREC-COLING 2024.

ECONLP addresses all themes at the interdisciplinary intersection of economics, finance, corporate social responsibility and NLP. The focus of the workshop will be on how NLP impacts business relations and procedures, economic transactions, monetary decision-making of the banking system, trading behavior at stock exchanges, and the roles of human and computational actors involved in these commercial and financial activities.

Important Dates

  • February 29, 2024 Workshop Papers Due (EXTENDED!)
  • March 15, 2024 Notification of acceptance
  • March 27, 2024 Camera-ready papers
  • May 20, 2024 Workshop date (NEW DATE!).

    [Update] We are pleased to announce that ECONLP will be joining forces with FinNLP-KDF in a larger, joint workshop!


We invite two types of original and unpublished works: Long papers (8 pages) should describe solid results with strong experimental, empirical or theoretical/formal backing, short papers (4 pages) should describe work in progress where preliminary results have already been worked out. Accepted papers will appear in the workshop proceedings. All papers are allowed unlimited but a reasonable number of pages for references. Final camera-ready versions will be allowed an additional page of content to address reviewers’ comments. All submissions must be anonymized, in PDF format (using the LREC-COLING 2024 style sheets for the main conference) and must be made through the Softconf website set up for this workshop (will be opened soon). When submitting a paper from the START page, authors will be asked to provide essential information about resources (in a broad sense, i.e. also technologies, standards, evaluation kits, etc.) that have been used for the work described in the paper or are a new result of your research. Moreover, ELRA encourages all LREC-COLING authors to share the described LRs (data, tools, services, etc.) to enable their reuse and replicability of experiments (including evaluation ones).

Topics of interest for the workshop include, but are not limited to the following themes:

  • NLP-based (stock) market analytics, e.g., prediction of economic performance indicators (trend prediction, performance forecasting, etc.), by analyzing verbal statements of enterprises, businesses, companies, and associated legal or administrative actors
  • NLP-based product analytics, e.g., based on social and mass media monitoring, summarizing reviews, classifying and mining complaint messages and other (non)verbal types of customer reactions to products or services
  • NLP-based customer analytics, e.g., client profiling, tracking product/company preferences, screening customer reviews or complaints
  • NLP-based organization/enterprise analytics (e.g., risk prediction, fraud analysis, predictive analysis of annual business, analysis of financial and corporate social responsibility reports, etc.)
  • NLP-based ESG-analytics, e.g., information extraction and sentiment analysis of Environmental, Social and Governance-related text and social media posts
  • NLP-based analysis of macro-economic phenomena in which national economies and the (inter)national banking system (IMF, Fed, PBoC, ECB) play an influential role
  • Market sentiments and emotions as evident from consumers’ and enterprises’ verbal behavior and their communication strategies about products and services
  • Relationship and interaction between quantitative (structured) economic data (e.g., contained in time series data) and qualitative (unstructured verbal) economic data (press releases, newswire streams, social media contents, conference call statements, etc.)
  • Credibility and trust models for business agents involved in economic processes (e.g., as traders, sellers, advertisers) extracted from legacy data of their communication behavior
  • Deceptive or fake information recognition (fact or claim checking) related to economic objects (such as products, advertisements, etc.) or economic actors (such as industries, companies, reviewers, etc.), including opinion spam targeting at or emanating from economic actors and processes
  • Verbally fluent software agents (chatbots for sales and marketing) as virtual actors in economic processes, e.g., embodying models of persuasion, information biases, (un)fair trading
  • Client-supplier interaction platforms (e.g., portals, helps desks, newsgroups) and transaction support systems based on written or spoken natural language communication
  • Information aggregation of economic data and opinion statements from large, heterogeneous sources (e.g., generation of review or meeting summaries, automatic threading of social media communication)
  • Economy-tuned language models (domain adaptation policies, prompting strategies, etc.), • Text generation in economic domains, e.g., review generation, complaint response generation
  • Generation and maintenance of knowledge graphs and ontologies for economics
  • Corpora and annotation policies (guidelines, metadata schemata, etc.) for economic NLP

Shared Task

For the first time in the history of ECONLP, we will also organize a shared task! A very important aspect of corporate reputation concerns Corporate Social Responsibility (CSR) content. Laws and regulations such as FCPA in the US, Sapin II and the UK Bribery Act have made companies even more liable for knowing about sustainability infractions. The focus of this shared task will be on "Cross-lingual Classification of Corporate Social Responsibility (CSR) Themes and Topics". This shared task will provide the NLP community with data sets in multiple languages (English, French, and simplified Chinese) for CSR news analysis and will shed light on the feasibility of crosslingual CSR theme detection. The community will also gain insights into fine-grained topic classification for two large CSR themes, viz. Environment (ENV) and Labour and Human Rights (LAB).

Check out our Shared Task website for more information!


  • Udo Hahn, TexKnowlogy, Germany (email)
  • Véronique Hoste, Ghent University, Belgium (email)
  • Sanjiv Ranjan Das, Santa Clara University, USA (email)

Program Committee

  • E. M. Ion Androutsopoulos — Athens University of Economics and Business, Athens, Greece
  • Ruihan Bao — Mizuho Securities Co., Ltd., Japan
  • Ruihai Dong — Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
  • Flavius Frasincar — Erasmus U Rotterdam, Rotterdam, The Netherlands
  • Petr Hájek — University of Pardubice, Pardubice, Czech Republic
  • Keiko Harimoto — Mizuho Securities Co., Ltd., Japan
  • Masanori Hirano — Preferred Networks Inc., Japan
  • Kiyoshi Izumi — The University of Tokyo, Tokyo, Japan
  • Wai Lam — Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
  • Qing Li — Southwestern University of Finance & Economics, Sichuan, Chengdu, China
  • Pekka Malo — Aalto University, Aalto, Finland
  • Puneet Mathur — University of Maryland, College Park, MD, USA
  • Igor Mozetič — Jožef Stefan Institute, Ljubljana, Slovenia
  • Senja Pollak — Jožef Stefan Institute, Ljubljana, Slovenia
  • Nicolas Pröllochs — Universität Gießen, Gießen, Germany
  • Samuel Rönnqvist — Zefort (formerly Aivan AI), Turku, Finland
  • Bryan R. Routledge — Carnegie Mellon University, Pittsburgh, PA, USA
  • Hiroki Sakaji — Hokkaido University, Hokudai, Japan
  • Kazuhiro Seki — Konan University, Kobe, Japan
  • Agam Shah — Georgia Institute of Technology, Atlanta, GA, USA
  • Kiyoaki Shirai — Japan Advanced Institute of Science & Technology (JAIST), Nomi, Japan
  • Rafet Sifa — Fraunhofer IAIS, Sankt Augustin, Germany
  • Ankur Sinha — Indian Institute of Management, Ahmedabad, India
  • Arnav Wadhwa — Chainlink Labs
  • Frank Z. Xing — National University of Singapore, Singapore