SPNLP 2022 : 6th Workshop on Structured Prediction for NLP

tl;dr:

submission deadline February 28, 2022 for new submissions via OpenReview, and
submission deadline March 12, 2022 for submissions already reviewed via ARR.

http://structuredprediction.github.io/SPNLP22


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:

Research papers

Position papers

Tutorial/overview papers


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.


Organizers

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)


Important Dates

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


CONTACT: [email protected]