SPNLP 2022 : 6th Workshop on Structured Prediction for NLP
Call For Papers
submission deadline February 28, 2022 for new submissions via OpenReview, and
submission deadline March 12, 2022 for submissions already reviewed via ARR.
Call for Papers
Deep learning is being increasingly used for NLP applications in academia and industry. Many NLP prediction tasks using neural and other probabilistic methods involve assigning values to mutually dependent variables. For example, when designing a model to automatically analyze the structure of a sentence, document or conversation (e.g., parsing, semantic role labeling, discourse analysis or dialogue slot filling), it is crucial to model the correlations between labels. Many other NLP tasks, such as machine translation, textual entailment, information extraction and summarization, can be also modeled as structured prediction problems.
In order to tackle such problems, various structured prediction approaches have been proposed, and their effectiveness has been demonstrated. Studying structured prediction is interesting from both NLP and machine learning (ML) perspectives. From the NLP perspective, syntax and semantics of natural language are clearly structured and advances in this area will enable researchers to understand the linguistic structure of data. From the ML perspective, the large amount of available text & graph/relational data and complex linguistic structures bring challenges to the learning community. Designing expressive yet tractable models and studying efficient learning and inference algorithms become important issues.
This workshop follows the five previous successful editions in 2021, 2020, 2019, 2017 and 2016 on Structured Prediction for NLP, as well as the closely related ICML 17 Workshop on Deep Structured Prediction. It is very timely, as there has been a renewed interest in structured prediction among NLP researchers due to recent advances in methods using continuous representations, able to learn with task-level supervision, or modeling latent linguistic structure.
Topics will include, but are not limited to the following:
Deep learning for structured prediction in NLP
Multi-task learning for structured output tasks
Reinforcement learning and imitation learning for structured learning in NLP
Deep learning on graphs & relational data (graph neural networks)
Graph embedding methods for Knowledge Graphs
Learning structured representations (e.g., relations, graphs) from language data
Reasoning with structured data for NLP tasks
Latent structured variable models
Structured deep generative models
Integer linear programming and other modeling techniques
Approximate inference for structured prediction
Structured training for non-linear models
Structured prediction software
Structured prediction applications in NLP
We invite submissions of the following kinds:
Long/short papers should consist of eight/four pages of content plus unlimited pages for bibliography. Submissions must be in PDF format following the official ACL style templates, anonymized for review. Papers can be submitted as non-archival, so that their content can be reused for other venues. Add “(NON-ARCHIVAL)” to the title of the submission. Non-archival papers will be linked from the workshop's webpage if their authors wish to. Previously published work can also be submitted as non-archival in the same way, with the additional requirement to state on the first page the original publication.
Submission is electronic and is managed by OpenReview at https://openreview.net/group?id=aclweb.org/ACL/2022/Workshop/SPNLP
Reviewing will be double-blind, and thus no author information should be included in the papers; self-reference should be avoided as well. Each submission will be reviewed by at least 2 program committee members.
Andreas Vlachos (University of Cambridge, UK)
Priyanka Agrawal (Booking.com, Netherlands)
André Martins (Unbabel and Instituto de Telecomunicações, Portugal)
Gerasimos Lampouras (Huawei Noah's Ark Lab, UK)
Chunchuan Lyu (University of Lisbon, Portugal)
February 28, 2022: Due date for submissions via OpenReview directly for review
March 12, 2022: Due date for submissions already reviewed via ARR
March 26, 2022: Notification of Acceptance
April 10, 2022: Camera-ready papers due
May 27, 2022: Workshop Date