To improve deep learning models' robustness, adversarial training has been frequently used in computer vision with satisfying results. However, adversarial perturbation on text have turned out to be more challenging due to the discrete nature of text. The generated adversarial text might not sound natural or does not preserve semantics, which is the key for real world applications where text classification is based on semantic meaning. In this paper, we describe a new way for generating adversarial samples by using pseudo-labeled in-domain text data to train a seq2seq model for adversarial generation and combine it with paraphrase detection. We showcase the benefit of our approach for a real-world Natural Language Understanding (NLU) task, which maps a user's request to an intent. Furthermore, we experiment with gradient-based training for the NLU task and try using token importance scores to guide the adversarial text generation. We show that our approach can generate realistic and relevant adversarial samples compared to other state-of-the-art adversarial training methods. Applying adversarial training using these generated samples helps the NLU model to recover up to 70% of these types of errors and makes the model more robust, especially in the tail distribution in a large scale real world application.

Semi-supervised Adversarial Text Generation based on Seq2Seq models

**Topics:** Commonsense Reasoning 
Computational Social Science and Cultural Analytics 
Dialogue and Interactive Systems 
Discourse and Pragmatics 
Efficient Methods for NLP 
Information Extraction 
Information Retrieval and Text Mining 
Interpretability, Interactivity and Analysis of Models for NLP 
Interpretability, Interactivity and Analysis of Models for NLP (CL) 
Language Modeling and Analysis of Language Models 
Machine Learning for NLP 
Machine Translation

Poster Session 3

poster

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/9eec7d4a287ee18c278b08229290aa83.png)](https://drive.google.com/file/d/1OlPv6QBeo62VVTughj2jkiLeyHd1WnUt/view)
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/a3db7a768409f05192210d98601edb25.png)](https://emnlp2022.rocket.chat/)

To access this site you need to register. Please register [here](https://2022.emnlp.org/registration/).

Register here

EMNLP 2022

Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

Hieu Le

1

3

SHORT BIO

Presentations

Semi-supervised Adversarial Text Generation based on Seq2Seq models

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES