United States

NAACL 2022

Controlled Data Generation via Insertion Operations for NLU

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

poster

Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model

Efficient Semi-supervised Consistency Training for Natural Language Understanding

Distantly Supervised Aspect Clustering And Naming For E-Commerce Reviews

CULG: Commercial Universal Language Generation

Constraining word alignments with posterior regularization for label transfer

Explaining the Effectiveness of Multi-Task Learning for Efficient Knowledge Extraction from Spine MRI Reports

Asynchronous Convergence in Multi-Task Learning via Knowledge Distillation from Converged Tasks

Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores

Retrieval Based Response Letter Generation For a Customer Care Setting

Knowledge extraction from aeronautical messages (NOTAMs) with self-supervised language models for aircraft pilots

https://d3smihljt9218e.cloudfront.net/transcript/36463/subtitle/ef46488c466b965fb040a29318476821.vtt

English (automatic)

Controlled Data Generation via Insertion Operations for NLU

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