[Commlist] Call for papers: 1st Workshop on Novel Evaluation Approaches for Text Classification Systems on Social Media

1st Workshop on Novel Evaluation Approaches for Text Classification 
Systems on Social Media

Co-located with ICWSM 2022, 6 June 2022, Hybrid format - Atlanta, 
Georgia (US) and online

https://neatclass-workshop.github.io/

The automatic or semiautomatic analysis of textual data is a key 
approach to analyse the massive amounts of user-generated content 
online, from the identification of sentiment in text and topic 
classification to the detection of abusive language, misinformation or 
propaganda. However, the development of such systems faces a crucial 
challenge. Static benchmarking datasets and performance metrics are the 
primary method for measuring progress in the field, and the publication 
of research on new systems typically requires demonstrating an 
improvement over state-of-the-art approaches in this way. Yet, these 
performance metrics can obscure critical failings in current models. 
Improvements in metrics often do not reflect improvements in the 
real-world performance of models. There is clearly a need to rethink 
performance evaluation for text classification and analysis systems to 
be usable and trustable.

If unreliable systems achieve astonishing scores with traditional 
metrics, how do we recognise progress when we see it? The goal of the 
Workshop on Novel Evaluation Approaches for Text Classification Systems 
on Social Media (NEATCLasS) is to promote the development and use of 
novel metrics for abuse detection, hate speech recognition, sentiment 
analysis and similar tasks within the community, to better be able to 
measure whether models really improve upon the state of the art, and to 
encourage a wide range of models to be tested on these new metrics.

Recently there have been attempts to address the problem of benchmarks 
and metrics that do not represent performance well. For example, in 
abusive language detection, there are both static datasets of 
hard-to-detect examples (Röttger et al. 2021) and dynamic approaches for 
generating such examples (Calabrese et al. 2021). On the platform 
DynaBench (Kiela et al. 2021), benchmarks are dynamic and constantly 
updated with hard-to-classify examples, avoiding overfitting a 
predetermined dataset. However, these approaches only capture a tiny 
fraction of issues with benchmarking. There is still much work to do.

For the first edition of the workshop on Novel Evaluation Approaches for 
Text Classification Systems (NEATCLasS) we welcome submissions 
discussing such new evaluation approaches, introducing new or refining 
existing ones, promoting the use of novel metrics for abuse detection, 
sentiment analysis and similar tasks within the community. Furthermore, 
the workshop will promote discussion on the importance, potential and 
danger of disagreement in tasks that require subjective judgements. This 
discussion will also focus on how to evaluate human annotations, and how 
to find the most suitable set of annotators (if any) for a given 
instance and task. The workshop will solicit, among others, research 
papers about
* Issues with current evaluation metrics and benchmarking datasets
* New evaluation metrics
* User-centred (qualitative or quantitative) evaluation of social media 
text analysis tools
* Adaptations and translations of novel evaluation metrics for other 
languages
* New datasets for benchmarking
* Increasing data quality in benchmarking datasets, e.g., avoidance of 
selection bias, identification of suitable expert human annotators for 
tasks involving subjective judgements
* Systems that facilitate dynamic evaluation and benchmarking
* Models that perform better at hard-to-classify instances and novel 
evaluation metrics such as AAA, DynaBench and HateCheck
* Bias, error analysis and model diagnostics
* Phenomena not captured by existing evaluation metrics (such as models 
making the right predictions for the wrong reason)
* Approaches to mitigating bias and common errors
* Alternative designs for NLP competitions that evaluate a wide range of 
model characteristics (such as bias, error analysis, cross-domain 
performance)
* Challenges of downstream applications (in industry, computational 
social science, computational communication science, and others) and 
reflections on how these challenges can be captured in evaluation metrics

Format and Submissions

The workshop will take place as a full-day meeting on 6 June. 
Participants will be invited to trial an innovative format for paper 
presentations: presenters will be given 5 minutes to describe their 
research questions and hypotheses, and a group discussion will start 
after that. Then, presenters will be given 5 more minutes to describe 
their method and results, followed by a new group discussion about the 
interpretation and implications of such results. In the afternoon there 
will be collaborative group activities to bring researchers together and 
collect ideas for new evaluation approaches and future work in the 
field. We will discuss how we can organise competitions when there are 
multiple evaluation metrics and benchmarking datasets are dynamic.

We invite research papers (8 pages), position and short papers (4 
pages), and demo papers (2 pages). Submissions must be original and 
should not have been published previously or be under consideration for 
publication while being evaluated for this workshop. Submissions will be 
evaluated by the program committee based on the quality of the work and 
its fit to the workshop themes. All submissions should be double-blind 
and a high-resolution PDF of the paper should be uploaded to the 
EasyChair submission site (link TBD) before the paper submission 
deadline. All papers must be submitted, and formatted in AAAI 
two-column, camera-ready style. Authors of accepted papers will have the 
opportunity to publish their papers through workshop proceedings by the 
AAAI Press. Submission instructions will be uploaded to the workshop web 
page in due course: https://neatclass-workshop.github.io/

Timeline
* Submission link: TBD – see https://neatclass-workshop.github.io/
* Papers submission deadline: March 27, 2022
* Paper acceptance notification: April 10, 2022
* Final camera-ready paper due: April 17, 2022
* Workshop Day: June 6, 2022

Organisers

Björn Ross, University of Edinburgh (Contact: (b.ross /at/ ed.ac.uk))
Roberto Navigli, Sapienza University of Rome
Agostina Calabrese, University of Edinburgh

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