2nd WIT: Workshop On Deriving Insights From User-Generated Text @ACL2022
https://megagon.ai/2nd-workshop-on-deriving-insights-from-user-generated-text-wit/

Workshop Overview
Every day, millions of people use Web platforms and online services, generating massive amounts of data about products and services. This data can be structured such as in tables; semi-structured such as in call-center records and dialogues; or unstructured such as in customer reviews and forums. The goal of this workshop is to advance research over user-generated text.

Recent progress in natural language processing, machine learning, knowledge bases and database management have demonstrated promising results and far-reaching uses of text. However, there is tremendous untapped potential in exploring and exploiting advanced AI/ML/NLP techniques on user-generated text, which is rich in user insights and experiences.

The goal of this workshop is to bring together researchers and practitioners in this area, to clarify impactful research problems, share findings from adaptation of existing approaches to user-generated data, and generate new ideas for future research. We seek papers that address challenges in harnessing user-generated data.

Confirmed Speaker (alphabetically ordered list):
Nina Balcan – Carnegie Mellon University
Carlos Guestrin – Stanford University
Mirella Lapata – University of Edinburgh
Rada Mihalcea – University of Michigan, Ann Arbor

Call For Submissions
We invite submissions of long and short papers on original and unpublished work that address challenges around harnessing textual user-generated data, such as that found in online reviews, web forums, and crowdsourced data. The topics include but are not limited to:
* Information extraction from user-generated text
* Domain adaptation to user-generated text
* Data cleaning
* Entity matching in user-generated text
* Semantic Search
* Robustness to noise
* Summarization of user-generated text
* Language generation
* (Commonsense) knowledge bases from user-generated text
* Information Seeking QA/Dialogue

All machine learning, text mining, and natural language processing techniques are welcome. All short and long papers should follow the ACL 2022 style guidelines and multiple submission policy (https://www.2022.aclweb.org/callpapers). For more information about templates, guidelines, and instructions, see the ARR CFP guidelines (https://aclrollingreview.org/cfp). You can submit your papers using the ARR platform (https://openreview.net/group?id=aclweb.org/ACL/2022/Workshop/WIT).

All accepted short and long papers must be presented as talk/poster/demo at the workshop, depending on the workshop schedule. At least one author of each accepted paper must register for ACL 2022 and attend the workshop.

Important Dates
Feb. 28, 2022: Workshop Paper Due Date
March 26, 2022: Notification of Acceptance
April 10, 2022: Camera-ready papers due

Mentorship Program
We will be hosting a mentorship program to facilitate exchange between authors and experts working in areas relevant to the workshop. The goal of this program is to foster collaborations and increase the quality and impact of the submissions.

Mentors are expected to guide mentees during the mentorship period as they prepare submissions for this workshop. Their interactions may include an in-depth discussion of related work to ensure submissions are well contextualized, discussions on writing and presentation of the submission, and discussions about possible extensions of the submissions. Mentees are expected to prepare the submissions by Feb. 7, 2022 and contact their assigned mentor. Mentors and mentees are encouraged to dedicate at least 4hrs over the course of the program to maximize the benefits of the program. They can meet virtually within the first week after the mentor-mentee matching is made and set expectations for subsequent meetings. Their efforts should culminate in a final version of the paper that should be submitted by the deadline.

Applications to the mentorship program are due by Feb. 1, 2022.

Application to be a mentor/mentee: https://forms.gle/F52Uz41LugmaHntr6

Organizers
Estevam Hruschka, Megagon Labs (estevam@megagon.ai)
Tom Mitchell, Carnegie Mellon University, (tom.mitchell@cmu.edu)
Marko Grobelnik, Jozef Stefan Institute (JSI), (marko.grobelnik@ijs.si)
Dunja Mladenic, Jozef Stefan Institute (JSI), (dunja.mladenic@ijs.si)
Nikita Bhutani, Megagon Labs (nikita@megagon.ai)

Contact
If you have any questions or inquiries regarding the workshop or need further information, please do not hesitate to send an email to wit@megagon.ai or access the workshop website: https://megagon.ai/2nd-workshop-on-deriving-insights-from-user-generated-text-wit/

Program Committee
Sara Abdali – UC Riverside
Shabnam Behzad – Georgetown University
Brett Zhiyuan Chen – Google
Maisa Duarte – Bradesco Bank – Brazil
Nelson Ebecken – COPPE/UFRJ Federal University of Rio de Janeiro – Brazil
Jacob Eisenstein – Google
Joao Gama – University of Porto – Portugal
Tianyu Jiang – University of Utah
Hannah Kim – Megagon Labs
Aljaz Kosmerlj – Jozef Stefan Institute – Slovenia
Thom Lake – Indeed.com
Yutong Li – Apple
Jun Ma – Amazon
Vagelis Papalexakis – UC Riverside
Sajjadur Rahman – Megagon Labs
Yutong Shao – UC San Diego
Evan Shieh – Amazon
Xiaolan Wang – Megagon Labs
Chris Welty – Google
Natasha Zhang Foutz – University of Virginia