United States

NAACL 2022

Towards Open-Domain Topic Classification.

## 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

SETSum: Summarization and Visualization of Student Evaluations of Teaching

Human-machine Interface for Few-shot Rule Synthesis for Information Extraction.

RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios

Contrastive Explanations of Text Classifiers as a Service

Flowstorm: Open-Source Platform with Hybrid Dialogue Architecture

ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations.

TurkishDelightNLP: A Neural Turkish NLP Toolkit

Web-based Annotation Interface for Derivational Morphology

textless-lib: a Library for Textless Spoken Language Processing

We investigate how well BERT performs on predicting factuality in several existing English datasets, encompassing various linguistic constructions. Although BERT obtains a strong performance on most datasets, it does so by exploiting common surface patterns that correlate with certain factuality labels, and it fails on instances where pragmatic reasoning is necessary. Contrary to what the high performance suggests, we are still far from having a robust system for factuality prediction.

He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on Pragmatics

demo

https://d3smihljt9218e.cloudfront.net/transcript/36557/subtitle/12715cca7d9e2af95bb74afd5f10519f.vtt

English (automatic)

Towards Open-Domain Topic Classification.

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