Dear Colleagues,
*** Apologies for cross-posting ***
Call for papers: Explainable AI in Natural Language Processing
Traditional Natural Language Processing (NLP) models (e.g., decision trees,
Markov models, etc.) have primarily been based on techniques that are
inherently interpretable models, referred to as white-box techniques. However,
in recent years, NLP models have employed advanced neural approaches along with
language embedding features. Using these advanced approaches, mostly referred
to as black-box techniques, the NLP models have yielded state-of-art
performance. Nonetheless, the level of interpretability (e.g., how the model
arrives at its results) has reduced significantly. This obfuscated
interpretability not only lowers the end users’ trust in the NLP models but
also makes it challenging for the developers to debug or improve by analyzing
the models for further improvement. Therefore, nowadays, researchers in the NLP
community are giving significant attention to the emerging field called
Explainable AI (XAI) to tackle the obfuscated complexity of AI systems for
trust and improvement. Apart from academia, organizations and companies also
have launched high-funding projects such as DARPA XAI, People +AI Research
(PAIR), etc.
As XAI is still a growing field, there is plenty of room for innovation to
improve the explainability of NLP systems. In recent works, explainable NLP
models have captured linguistic knowledge of neural networks, explain
predictions, stress-test models via challenge sets or adversarial examples, and
interpret language embeddings.
The goal of this Research Topic is to better understand the present status of
the XAI in NLP by identifying: new dimensions for a better explanation,
evaluation techniques used to measure the quality of explanations, approaches
or developments of new software toolkits to explain XAI in NLP, and transparent
deep learning models for different NLP task.
The scope of this Research Topic covers (but is not restricted to) the
following topics:
• Survey of XAI in NLP in general or any particular NLP task such as
NER, QA, Sentiment analysis, social media (SocialNLP), etc.
• Explainable Neural models in Machine Translation
• Explainable Neural models in Named Entity Recognition
• Explainable Neural models in Question Answering
• Explainable Neural models in Sentiment Analysis
• Explainable Neural models in Opinion Mining
• Explainable Neural models in SocialNLP
• Evaluation techniques used to measure the quality of explanations
• Tools for explaining explainability
• Resources related to XAI in the context of NLP
The Research Topic welcomes contributions toward interpretable models for
efficient solutions to NLP research problems that explain the explainability of
the proposed model using suitable explainability technique(s) (e.g.,
example-driven, provenance, feature importance, induction, surrogate models,
etc.), visualization technique(s) (e.g., raw examples, saliency, raw
declarative, etc.), and other aspects. Software toolkits or approaches that can
help users express explainability to their models and ML pipelines are also
welcome.
The full Call for Papers is available at
https://www.frontiersin.org/research-topics/48440/explainable-ai-in-natural-language-processing
Impact of the publication: https://www.frontiersin.org/about/impact
* Manuscript Deadline:
* 19 May 2023, This is a mandatory deadline for your full manuscript
submission.
Guest Editors:
Somnath Banerjee (University of Tartu, [email protected])
David Tomás (University of Alicante, [email protected])
Somnath Banerjee
Lecturer,
Institute of Computer Science,
University of Tartu,
Narva mnt 18,
51009 Tartu, ESTONIA
webpage: http://www.ut.ee//~somnath/
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