The 4th Workshop on Data Science with Human-in-the-loop (DaSH) will be co-located with the EMNLP 2022 conference. The aim of this workshop is to stimulate research on cooperation between humans and computers within the broad area of natural language processing, including but not limited to information extraction, information retrieval and text mining, machine translation, dialog systems, question answering, language generation, summarization, model interpretability, evaluation, fairness, and ethics. We invite researchers and practitioners interested in understanding how to optimize human-computer cooperation and how to minimize human effort along an NLP pipeline in a wide range of tasks and applications.
Goal:
This workshop will bring together interdisciplinary researchers from academia, research labs and practice to share, exchange, learn, and develop preliminary results, new concepts, ideas, principles, and methodologies on understanding and improving human-computer interaction in natural language processing. We expect the workshop to help develop and grow a strong community of researchers who are interested in this topic and to yield future collaborations and scientific exchanges across the relevant areas of computational linguistics, natural language processing, data mining, machine learning, data and knowledge management, human-machine interaction, and intelligent user interfaces.
Topics:
We invite submissions describing innovations and implementations that focus on understanding how to optimize human-computer cooperation and minimize human effort along the data science pipeline in a wide range of data science tasks and real-world applications.
Topics of interest to this workshop include, but are not limited to the following.
-- Supervised, self-supervised, transfer, and unsupervised learning with human-in-the-loop
-- Human intervention and consequences on model bias, overfitting, and fairness
-- Human and computer cooperation in data cleaning, preparation, representation for NLP tasks
-- Optimizing human effort in data labeling
-- Crowdsourcing in NLP
-- Human-in-the-loop during model evaluation and model improvement
-- Multi-modal human-in-the-loop issues in NLP in conjunction with data from other modalities such as social media, -- knowledge graphs, speech, image, video
-- Enabling non-experts to build advanced NLP models
-- Formal and higher abstractions for human-machine interaction in NLP tasks
-- User interfaces for data preprocessing, labeling, and NLP model building, evaluation, and interpretation
-- Human-in-the-loop issues in implementing, using, evaluating, and deploying NLP models in specific applications (e.g., medical, scientific, legal, business, digital humanities, creative).
Submission:
Authors are invited to submit either of the following:
-- Original, unpublished research papers that are not being considered for publication in any other forum. Research papers are limited to six pages in length, excluding references.
-- Short papers of late-breaking work and work in progress. Abstracts are limited to two pages.
Authors of both types of papers will need to present these papers at DaSH. Submissions to the workshop must be in PDF and should follow the EMNLP paper formatting. All submissions should be anonymized to facilitate double-blind reviewing.
Important Dates:
Submission deadline: September 7, 2022
Notification: October 9, 2022
Camera ready: October 16, 2022
Workshop Dates: December 8, 2022
Contact:
dash21 [at] softconf.com