Call for System Demonstrations

The EMNLP 2022 System Demonstration Program Committee invites proposals for the Demonstrations Program. Demonstrations may range from early research prototypes to mature production-ready systems. Of particular interest are publicly available open-source or open-access systems. We additionally strongly encourage demonstrations of industrial systems that are technologically innovative given the current state of the art of theory and applied research in natural language processing. Each submitted demonstration must be accompanied by a submitted paper describing the system (see below).

Areas of interest include all topics related to theoretical and applied natural language processing, such as (but not limited to) the topics listed on the main conference website.

We especially welcome systems for model analysis and interpretation. Submitted systems may be of the following types:

  • Natural language processing systems or system components
  • Application systems using language technology components
  • Software tools for natural language processing research
  • Software for demonstration or evaluation
  • Software supporting learning or education
  • Tools for data visualization and annotation
  • Tools for model inspection
  • Development tools

Papers describing accepted demonstrations will be published in a companion volume of the EMNLP 2022 conference proceedings. Please note: Commercial sales and marketing activities are not appropriate in the Demonstrations Program and should be arranged as part of the Exhibit Program.

Best Demo Award

As in previous years, EMNLP 2022’s demo track will feature a Best Demo Award. The award is for the best demo, and not for the best paper. That is to say, it will be judged by a person who actually interacts with the demo.

Important Dates

  • Paper submission deadline: August 1, 2022
  • Notification of acceptance: September 18, 2022
  • Camera-ready submission: October 15, 2022

All deadlines are 11.59 pm UTC -12h (“anywhere on Earth”).

Submission of papers describing demonstrations

A paper submitted to accompany a demonstration should outline the design of the system and provide sufficient details to allow the evaluation of its validity, quality, and relevance to natural language processing. A paper can do this by addressing the following questions:

  • What problem does the proposed system address?
  • Why is the system important and what is its impact?
  • What is the novelty in the approach/technology on which this system is based?
  • Who is the target audience?
  • How does the system work?
  • How does it compare with existing systems?
  • How is the system licensed?

Paper submission is electronic, using the Softconf START conference management system.

Style files should meet the requirements of the EMNLP main conference. Submissions may consist of up to 6 pages, plus unlimited references. Submissions must conform to the EMNLP 2022 official style guidelines and they must be in PDF format. Submissions need to describe original, unpublished work, as publication in EMNLP will be archival.

Multiple Submission Policy

We follow the Multiple Submission Policy of the CFP of the EMNLP 2022 main conference. The paper cannot be submitted elsewhere, while in review at EMNLP 2022.

Reviewing Policy

Reviewing will be single-blind, so authors do not need to conceal their identity. The paper should include the authors’ names and affiliations. Self-references are also allowed.

Ethics Policy

Authors are required to honor the ethical code set out in the ACM Code of Ethics. The ethical impact of our research, the use of data, and potential applications of our work have always been an important consideration, and as artificial intelligence is becoming more mainstream, these issues are increasingly pertinent. We ask that all authors read the code, and ensure that their work is conformant to this code. We reserve the right to reject papers on ethical grounds, where the authors are judged to have operated counter to the code of ethics, or have inadequately addressed legitimate ethical concerns about their work.

Authors will be allowed extra space after the 6th page for a broader impact statement or other discussion of ethics. The EMNLP demonstration review form will include a section addressing these issues and papers flagged for ethical concerns by reviewers will be further reviewed by an ethics committee. Note that an ethical considerations section is not required, but papers working with sensitive data or on sensitive tasks that do not discuss these issues will not be accepted. Conversely, the mere inclusion of an ethical considerations section does not guarantee acceptance. In addition to acceptance or rejection, papers may receive a conditional acceptance recommendation. Camera-ready versions of papers designated as conditional accept will be re-reviewed by the ethics committee to determine whether the concerns have been adequately addressed. Please read the ethics FAQ (shared with the main conference) for more guidance on some problems to look out for and key concerns to consider in relation to the code of ethics.

Demo Details

As the conference will be hybrid, we strongly recommend that all demos are provided via one of the following formats: (1) A live demo website; or (2) A website with a downloadable installation package of the demo; unless this is impossible because some special hardware is required or access is otherwise limited.

You are required to submit a short (~2 minute) screencast video demonstrating the system together with your paper submission. This screencast will be used to evaluate the paper, but won’t be published unless requested. We encourage the authors to include visual aids (e.g., screenshots, snapshots, or diagrams) in the paper. Authors will also be able to upload and submit additional material, if needed. Please upload the video to some hosting site (YouTube, Vimeo, etc.) and include the link in your paper submission. To ensure accessibility for deaf or hard-of-hearing viewers, we encourage authors to caption videos prior to submission.

Demo co-chairs

Wanxiang Che (Harbin Institute of Technology, China)

Ekaterina Shutova (University of Amsterdam, Netherlands)