Ireland

ACL 2022

Zero- and Few-Shot NLP with Pretrained Language Models - Prompting &amp; In-context learning

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
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, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 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 will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

Zero- and Few-Shot NLP with Pretrained Language Models - Prompting & In-context learning

tutorial

News events are often associated with {\em quantities} (e.g., the number of COVID-19 patients or the number of arrests in a protest), and it is often important to extract their type, time, and location from unstructured text in order to analyze these quantity events. This paper thus formulates the NLP problem of spatiotemporal quantity extraction, and proposes the first meta-framework for solving it. This meta-framework contains a formalism that decomposes the problem into several information extraction tasks, a shareable crowdsourcing pipeline, and transformer-based baseline models. We demonstrate the meta-framework in three domains---the COVID-19 pandemic, Black Lives Matter protests, and 2020 California wildfires---to show that the formalism is general and extensible, the crowdsourcing pipeline facilitates fast and high-quality data annotation, and the baseline system can handle spatiotemporal quantity extraction well enough to be practically useful. We release all resources for future research on this topic.\footnote{\url{https://github.com/steqe}}

A Meta-framework for Spatiotemporal Quantity Extraction from Text

Zero- and Few-Shot NLP with Pretrained Language Models - Gradient-based LM task adaptation

Zero- and Few-Shot NLP with Pretrained Language Models - Evaluation and benchmarks 

Zero- and Few-Shot NLP with Pretrained Language Models - Meta-training

Zero- and Few-Shot NLP with Pretrained Language Models -  Pretraining considerations for zero/few-shot

Vision-Language Pretraining:  Current Trends and the Future: Vision-language landscape before the pretraining era

Vision-Language Pretraining:  Current Trends and the Future: Modern vision-language pretraining

Vision-Language Pretraining:  Current Trends and the Future: Beyond statistical learning

A Gentle Introduction to Deep Nets and Opportunities for the Future: Simple Uses of Deep Nets

A Gentle Introduction to Deep Nets and Opportunities for the Future: Opportunities For The Future

technical paper

https://d3smihljt9218e.cloudfront.net/transcript/35827/subtitle/631fd6afbd1009c4b1c1d5522c44a2f5.vtt

English (automatic)

Zero- and Few-Shot NLP with Pretrained Language Models - Prompting & In-context learning

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A Meta-framework for Spatiotemporal Quantity Extraction from Text

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