United States

NAACL 2022

NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension

question answering system

machine reading comprehension

named entity recognition

transformers

## 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

Users often leave feedback on a myriad of aspects of a product which, if leveraged successfully, can help yield useful insights that can lead to further improvements down the line. Detecting actionable insights can be challenging owing to large amounts of data as well as the absence of labels in real-world scenarios. In this work, we present an aggregation and graph-based ranking strategy for unsupervised detection of these insights from real-world, noisy, user-generated feedback. Our proposed approach significantly outperforms strong baselines on two real-world user feedback datasets and one academic dataset.

What Do Users Care About? Detecting Actionable Insights from User Feedback

Developing a Production System for Purpose of Call Detection in Business Phone Conversations

Adversarial Text Normalization

Constraint-based Multi-hop Question Answering with Knowledge Graph

Fast Bilingual Grapheme-To-Phoneme Conversion

Knowledge Extraction From Texts Based on Wikidata

AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry

Parameter-efficient Continual Learning Framework in Industrial Real-time Text Classification System

Fast and Light-Weight Answer Text Retrieval in Dialogue Systems

BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations

https://d3smihljt9218e.cloudfront.net/transcript/36481/subtitle/bf0243cf23620fab41a61b80fd8f8bcd.vtt

English (automatic)

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What Do Users Care About? Detecting Actionable Insights from User Feedback

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