2020 The 6th Workshop on Noisy User-generated Text (W-NUT)

Nov 19, 2020 -- WNUT workshop is going virtual together with EMNLP 2020

The WNUT workshop focuses on Natural Language Processing applied to noisy user-generated text, such as that found in social media, online reviews, crowdsourced data, web forums, clinical records and language learner essays. The workshop hashtag is #wnut.

News! We will hold our workshop completely live online (registration for EMNLP 2020 is now open) -- 4 live invited talks with QA, 1-min or 5-min live talks for 33 regular papers, as well as interactive social event for two different time zones (4:00-8:00 GMT and 15:00-19:00 GMT -- click for a more detailed schedule). We accepted 33 regular workshop papers and 47 shared-task papers.

We are organizing three shared-tasks:

(1) Entity and relation recognition over wet-lab protocols. Data is released on June 08, 2020! Official evaluation will be August 31 ~ September 4 (entity) and September 9 ~ September 15 (relation), 2020.

(2) Identification of informative COVID-19 English Tweets. Data is released on June 21, 2020! Official evaluation will be August 17 ~ 21, 2020.

(3) COVID-19 Event Extraction from Twitter. Data is released on June 22, 2020! Official evaluation will be September 7 ~ 11, 2020.

Congratulations to the winners of the best paper awards, which are sponsored by Twitter this year:

Workshop Organizers

Invited Speakers

Important Dates

Program (accepted papers)

May I Ask Who’s Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance
Micaela Kaplan
"Did you really mean what you said?" : Sarcasm Detection in Hindi-English Code-Mixed Data using Bilingual Word Embeddings
Akshita Aggarwal, Anshul Wadhawan, Anshima Chaudhary and Kavita Maurya
Noisy Text Data: Achilles’ Heel of BERT
Ankit Kumar, Piyush Makhija and Anuj Gupta
Determining Question-Answer Plausibility in Crowdsourced Datasets Using Multi-Task Learning
Rachel Gardner, Maya Varma, Clare Zhu and Ranjay Krishna
Combining BERT with Static Word Embeddings for Categorizing Social Media
Israa Alghanmi, Luis Espinosa Anke and Steven Schockaert
Enhanced Sentence Alignment Network for Efficient Short Text Matching
Zhe Hu, Zuohui Fu, Cheng Peng and Weiwei Wang
PHINC: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine Translation
Vivek Srivastava and Mayank Singh
Cross-lingual sentiment classification in low-resource Bengali language
Salim Sazzed
The Non-native Speaker Aspect: Indian English in Social Media
Rupak Sarkar, Sayantan Mahinder and Ashiqur KhudaBukhsh
Sentence Boundary Detection on Line Breaks in Japanese
Yuta Hayashibe and Kensuke Mitsuzawa
Non-ingredient Detection in User-generated Recipes using the Sequence Tagging Approach
Yasuhiro Yamaguchi, Shintaro Inuzuka, Makoto Hiramatsu and Jun Harashima
Generating Fact Checking Summaries for Web Claims
Rahul Mishra, Dhruv Gupta and Markus Leippold
Intelligent Analyses on Storytelling for Impact Measurement
Koen Kicken, Tessa De Maesschalck, Bart Vanrumste, Tom De Keyser and Hee Reen Shim
An Empirical Analysis of Human-Bot Interaction on Reddit
Ming-Cheng Ma and John P. Lalor
Detecting Trending Terms in Cybersecurity Forum Discussions
Jack Hughes, Seth Aycock, Andrew Caines, Paula Buttery and Alice Hutchings
Service registration chatbot: collecting and comparing dialogues from AMT workers and service’s users
Luca Molteni, Mittul Singh, Juho Leinonen, Katri Leino, Mikko Kurimo and Emanuele Della Valle
Automated Assessment of Noisy Crowdsourced Free-text Answers for Hindi in Low Resource Setting
Dolly Agarwal, Somya Gupta and Nishant Baghel
Punctuation Restoration using Transformer Models for Resource-Rich and -Poor Languages
Tanvirul Alam, Akib Khan and Firoj Alam
Truecasing German user-generated conversational text
Yulia Grishina, Thomas Gueudre and Ralf Winkler
Fine-Tuning MT systems for Robustness to Second-Language Speaker Variations
Md Mahfuz Ibn Alam and Antonios Anastasopoulos
Impact of ASR on Alzheimer’s Disease Detection: All Errors are Equal, but Deletions are More Equal than Others
Aparna Balagopalan, Ksenia Shkaruta and Jekaterina Novikova
Detecting Entailment in Code-Mixed Hindi-English Conversations
Sharanya Chakravarthy, Anjana Umapathy and Alan W Black
Detecting Objectifying Language in Online Professor Reviews
Angie Waller and Kyle Gorman
Annotation Efficient Language Identification from Weak Labels
Shriphani Palakodety and Ashiqur KhudaBukhsh
Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach
Ben Eyre, Aparna Balagopalan and Jekaterina Novikova
Quantifying the Evaluation of Heuristic Methods for Textual Data Augmentation
Omid Kashefi and Rebecca Hwa
An Empirical Survey of Unsupervised Text Representation Methods on Twitter Data
Lili Wang, Chongyang Gao, Jason Wei, Weicheng Ma, Ruibo Liu and Soroush Vosoughi
Civil Unrest on Twitter (CUT): A Dataset of Tweets to Support Research on Civil Unrest
Justin Sech, Alexandra DeLucia, Anna L Buczak and Mark Dredze
Tweeki: Linking Named Entities on Twitter to a Knowledge Graph
Bahareh Harandizadeh and Sameer Singh
Representation learning of writing style
Julien Hay, Bich-Lien Doan, Fabrice Popineau and Ouassim AIT ELHARA
"A Little Birdie Told Me ... " - Social Media Rumor Detection
Karthik Radhakrishnan, Tushar Kanakagiri, Sharanya Chakravarthy and Vidhisha Balachandran
Paraphrase Generation via Adversarial Penalizations
Gerson Vizcarra and Jose Ochoa-Luna
WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab Protocols
Jeniya Tabassum, Wei Xu and Alan Ritter
WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
Dat Quoc Nguyen, Thanh Vu, Afshin Rahimi, Mai Hoang Dao, Linh The Nguyen and Long Doan

Call for Papers

We seek submissions of long and short papers on original and unpublished work (same page limit EMNLP main conference). All accepted submissions will be presented as pre-recorded talks at the workshop, following the EMNLP 2020 main conference (more details here).

Topics of interest include but are not limited to:

All submissions should conform to EMNLP 2020 style guidelines. Long and short paper submissions must be anonymized. Abstract submissions should include author information (and where the work was published in a footnote on the front page, if applicable). Please submit your papers at the SoftConf link.

Double Submission Policy: Papers that have been or will be submitted to other meetings or publications must indicate at submission time. Authors of a paper accepted for presentation must notify the workshop organizers by the camera-ready deadline as to whether the paper will be presented or withdrawn.

Shared task 1: Entity and Relation Extraction over Wet Lab Protocols

Lab protocols specify steps in performing a lab procedure. They are noisy, dense, and domain-specific. Automatic or semi-automatic conversion of protocols into machine-readable format benefits biological research. In this task, system entries are invited for event recognition and relation extraction over these lab protocols. Note that these protocols are written by researchers and lab technicians worldwide, some of which may contain non-standard language or spelling errors. Here's a sample of the input data:

Initial data is released on June 8, 2020. Please register here to receive future data for the official evaluation (Aug 31 - Sep 4, 2020).

Details on the shared task are here. Contacts: Jeniya Tabassum, Wei Xu, Alan Ritter.

Shared task 2: Identification of informative COVID-19 English Tweets

The goals of this shared task are: (1) To develop a language processing task that potentially impacts research and downstream applications, and (2) To provide the community with a new dataset for identifying informative COVID-19 English Tweets.

For this task, participants are asked to develop systems that automatically identify whether an English Tweet related to the novel coronavirus (COVID-19) is informative or not. Such informative Tweets provide information about recovered, suspected, confirmed and death cases as well as location or travel history of the cases. The dataset and systems developed for this shared task will be beneficial for the development of COVID-19 related monitoring systems.

Details on the shared task are here. Contacts: Dat Quoc Nguyen, Thanh Vu, Afshin Rahimi.

Shared task 3: Extracting COVID-19 Events from Twitter

People usually share a wide variety of information related to COVID-19 publicly on social media. For example, Twitter users often indicate when they might be at increased risk of COVID-19 due to a coworker or other close contact testing positive for the virus, or when they have symptoms but were denied access to testing. In this shared task, participants are invited to develop systems that automatically extract COVID-19 related events from Twitter using our newly built corpus. Here is an example of our annotated data:

Initial data has been released on June 22, 2020. Please register here to receive future data for the official evaluation (Sep 7 - Sep 11, 2020).

Details on the shared task are here. Contacts: Shi Zong, Wei Xu, Alan Ritter.

Program Committee