*The KDD '23 Workshop on Robust NLP for Finance (RobustFin)*
Financial services is a document-rich industry. This has led to great
demand for advanced NLP techniques to process financial corpora in order to
curate knowledge, augment human intelligence, and support industry
professionals in making informed business decisions. Recent advances in the
NLP domain have motivated the emergence of key applications such as the
automation of due diligence protocols, financial question answering (QA)
and reasoning, conversational AI in finance, summarizing business entity
earnings calls, and analyzing investment sentiment of social media posts.
Due to the unique industry standards and regulations, robustness is a
fundamental consideration underlying nearly all financial NLP applications.
The concept of robsustness may connote many specific definitions and
real-life considerations. In finance, the key concerns of robustness refer
to better generalizability, higher explainability, lower risk of
catastrophic failure in unforeseen circumstances, and diligent fairness
considerations. Common NLP models like large language models trained on
generic corpora usually deteriorate when processing financial texts despite
having achieved state-of-the-art results in other applications. The
business and regulatory requirements for explainability and the reasoning
behind decision makings for the financial industry are often
uncompromising, while many NLP modeling techniques still lack this
characteristic.
Based on years of research experience in this domain, the organizers
recapitulate the key aspects of these challenges: massive volume of data,
large variation in the data format, low signal-to-noise ratio, scarcity of
expert annotated datasets, task ambiguity, challenges regarding data
integrity and privacy, domain shift, and high performance requirements set
by industry and regulatory standards. While these challenges have been
studied in many generic contexts, we believe working on this vertical
high-stakes domain, i.e. finance, will help consolidate the development of
solutions. 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.Topics of Interest
The topics of the workshop include, but are 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.· Reconciling
structured and unstructured knowledge to improve model interpretability;
financial knowledge graph construction.· 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 modelsSubmission
GuidelinesWe 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 https://www.acm.org/publications/proceedings-template. 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
https://cmt3.research.microsoft.com/RobustFin2023. 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.Important Dates
- Paper abstract due: May 16, 2023 AoE
- Paper submission due: June 4, 2023 AoE
- Submission notification: June 23, 2023
- Workshop: August 7-8, 2023
Organizing Committee· Sameena Shah - JPMorgan AI Research·
Xiaodan
Zhu - Queen's University· Gerard de Melo - Hasso Plattner Institute
and the University of Potsdam· Armineh Nourbakhsh - JPMorgan AI
Research· Xiaomo Liu - JPMorgan AI Research· Zhiqiang Ma -
JPMorgan AI Research· Charese Smiley - JPMorgan AI Research·
Zhiyu Chen - MetaWorkshop Website
https://robustfin.github.io/2023/
*Contact*
For general inquiries about the workshop, please write to the organizers at
robustfin.works...@gmail.com.
--
Best,
Xiaomo
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