Call for Papers

The workshop welcomes original research submissions related to robustness and explainability research on NLP, with a focus on the financial domain and/or financial corpora, which will include, but not be limited to, the topics listed below. The workshop will welcome both position and regular research papers. The scope of the workshop includes, but is not limited to, the following areas:

  • Adversarial attacks and defense to improve NLP model robustness and privacy protection;
  • Transfer learning applications and their robustness and explainability studies;
  • Robustness research against concept drift, domain shift, and noisy datasets; NLP model generalization;
  • Language modeling on financial corpora including tabular and numerical data, and multi-modal modeling; numeracy and quantitative reasoning, fact verification, and QA over tabular data;
  • Few-shot learning and prompt methods;
  • Synthetic or genuine financial textual datasets and benchmarks;
  • Reasoning and textual entailment in financial applications;
  • Empirical studies on robust NLP model.

Submissions

We invite submissions of relevant work that be of interest to the workshop. All submissions must be original contributions that have not been previously published and that are not currently under review by other conferences or journals. Submissions will be peer reviewed, single-blinded. Submissions will be assessed based on their novelty, technical quality, significance of impact, interest, clarity, relevance, and reproducibility. All submissions must be in PDF format and follow the current ACM two-column conference format. We accept two types of submissions:

  • full research paper: no longer than 9 pages (including references, proofs, and appendixes).
  • short/poster paper: no longer than 4 pages(including references, proofs, and appendixes).

Submission will be accepted via Microsoft CMT. All accepted papers will be presented in the workshop. At least one author of each accepted submission must attend the workshop to present their work. Submission will be non-archival.