United States

NAACL 2022

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

low-cost and green nlp

implementation at speed and scale

case studies from design to deployment

## 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

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

Intent Discovery for Enterprise Virtual Assistants: Applications of Utterance Embedding and Clustering to Intent Mining

Lightweight Transformers for Conversational AI

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

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

https://d3smihljt9218e.cloudfront.net/transcript/36470/subtitle/3df34eef4d0eda5d0c9b1381ba015c84.vtt

English (automatic)

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

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

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