UCNLP 2022 : 1st Workshop on User-Centered NLP
Call For Papers
1st Workshop on User-Centered NLP
Workshop date: April 26, 2022
Workshop website: https://caisa.informatik.uni-marburg.de/user_nlp.html
Contact email: email@example.com
February 3rd - Submissions Due
March 3rd - Notification of Acceptance
April 26th - Workshop Date (online)
All the deadlines are 11:59 PM GMT -12.
The 1st User-Centered NLP Workshop will be held in conjunction with the Web Conference (WWW) 2022 virtually in Lyon, France.
Current NLP models are mostly trained as "one-size-fits-all", i.e. without explicitly consideration of the diversity in the use and interpretation of language among individuals or groups of individuals. Such user-level variation (delineated by, e.g., demographics, culture, user interests) can cause stylistic and even semantic disparities, which decrease dialogue coherence, harm fairness, and reduce model robustness. User-centered NLP can fill these gaps by explicitly taking these variations into account and focusing on user-level modeling tasks.
We aim to create a platform where researchers can present rising challenges in building user-centered NLP models. While we have data statement discussions and schema for document-level annotations, few studies have explored data schema and standards on the user-level. Moreover, conventional evaluation metrics and schemes for the document-level models may not be appropriate to capture the diversity and complexity of user-level information. The lack of ethically appropriate, standardized, and easily accessible evaluation data and metrics is perhaps the major hindrance to the development of this field, impeding the reproducibility of experimental results. Finally, user-centered NLP raises a wide range of important ethical questions, such as algorithmic fairness and user privacy. An informed discussion on these timely topics requires gathering at one table researchers who encounter stylistic disparities and user-level tasks directly or indirectly in their work.
The overarching questions that motivate this workshop are:
To what extent do stylistic variations indirectly impact downstream applications which were historically treated as stylistically uniform?
To what extent is it desirable to exploit individual variations to reduce demographic disparity, promote user-level models and personalize NLP applications?
To what extent recent advances in related areas including representation learning, domain adaptation and transfer learning can leverage individual variations, understand user intentions, customize NLP models, and deliver interpretable outputs for users’ specific needs?
How to better evaluate user-centric models to shed insight on user-level disparities and the impact of personalized models?
How to better achieve privacy-preserving user centric NLP models when it comes to a wide range of personalization and user level tasks?
How much user data is sufficient for the desired system performance?
A non-exhaustive list of proposed topics and applications of interest follows. Suggested topics include:
Effects of stylistic variation on downstream tasks
User-level distributional vector models
Personalization and user-aware natural language generation
Fairness and ethics in user-level tasks
User modeling and user behavior analysis
Effective approaches to evaluate user-level models
Interactive and personalized information retrieval
Challenges in user privacy and private user-centered models
Potential applications include:
User sociodemographic inference applications, together with their issues and risks
Personalized text generation
User modeling for health applications (e.g. mental health, preventive care)
Identifying trustworthiness and deception of users
Rhetoric and personalization (e.g. stylistic choices in political speeches, etc.)
Full research papers (up to 8 pages for main content)
Short research papers (up to 4 pages for main content)
Vision/Position papers (up to 4 pages for main content)
The workshop calls for full research papers (up to 8 pages + 2 pages of appendices + 2 pages of references), describing original work on the listed topics, and short papers (up to 4 pages + 2 pages of appendices + 2 pages of references), on early research results, new results on previously published works, demos, and projects. In accordance with Open Science principles, research papers may also be in the form of data papers and software papers (short or long papers). The former present the motivation and methodology behind the creation of data sets that are of value to the community; e.g., annotated corpora, benchmark collections, training sets. The latter presents software functionality, its value for the community, and its application to a non-specialist reader. To enable reproducibility and peer-review, authors will be requested to share the DOIs of the data sets and the software products described in the articles and thoroughly describe their construction and reuse.
The workshop will also call for vision/position papers (up to 4 pages + 2 pages of appendices + 2 pages of references) providing insights towards new or emerging areas, innovative or risky approaches, or emerging applications that will require extensions to the state of the art. These do not have to include results already, but should carefully elaborate on the motivation and the ongoing challenges of the described area.
Submissions for review must be in PDF format and must adhere to the ACM template and format. Submissions that do not follow these guidelines, or do not view or print properly, may be rejected without review.
The proceedings of the workshops will be published jointly with The Web Conference 2022 proceedings.
Submit your contributions following the link: https://easychair.org/cfp/UserNLP_2022
The deadline for submission is 11:59pm GMT -12 on Feb, 3rd, 2022.
Submissions will be peer reviewed in the double-blind format and evaluated on relevance to the community. The presentation format (talk or poster) will be decided based on scientific merit and potential interest to a broad audience.
Papers to appear in the workshop proceedings have to contain a creative and original work, which was not submitted elsewhere. You are allowed to submit an already submitted paper in case you are only interested in a non-archival presentation of your work. This, however, has to be clearly indicated at submission time.
Xiaolei Huang, University of Memphis
Lucie Flek, University of Marburg
Silvio Amir, Northeastern University
Diyi Yang, Georgia Tech
Charles Welch, University of Marburg
Ramit Sawhney, ShareChat AI
Franck Dernoncourt, Adobe Research