Although Deep Learning models have revolutionized the speech and audio processing field, they forced building specialist models for individual tasks and application scenarios. Deep neural models also bottlenecked dialects and languages with limited labeled data.
Self-supervised representation learning methods promise a single universal model to benefit a collection of tasks and domains. They recently succeeded in NLP and computer vision domains, reaching new performance levels while reducing required labels for many downstream scenarios. Speech representation learning is experiencing similar progress with three main categories: generative, contrastive, predictive. Other approaches relied on multi-modal data for pre-training, mixing text or visual data streams with speech. Although self-supervised speech representation is still a nascent research area, it is closely related to acoustic word embedding and learning with zero lexical resources. This tutorial session will present self-supervised speech representation learning approaches and their connection to related research areas. Since many of the current methods focused solely on automatic speech recognition as a downstream task, we will review recent efforts on benchmarking learned representations to extend the application of such representations beyond speech recognition. A hands-on component of this tutorial will provide practical guidance on building and evaluating speech representation models.

Self-supervised Representation Learning for Speech Processing

Automatic detection of toxic language plays an essential role in protecting social media users, especially minority groups, from verbal abuse. However, biases toward some attributes, including gender, race, and dialect, exist in most training datasets for toxicity detection. The biases make the learned models unfair and can even exacerbate the marginalization of people. Considering that current debiasing methods for general natural language understanding tasks cannot effectively mitigate the biases in the toxicity detectors, we propose to use invariant rationalization (InvRat), a game-theoretic framework consisting of a rationale generator and a predictor, to rule out the spurious correlation of certain syntactic patterns (e.g., identity mentions, dialect) to toxicity labels. We empirically show that our method yields lower false positive rate in both lexical and dialectal attributes than previous debiasing methods.

Mitigating Biases in Toxic Language Detection through Invariant Rationalization

Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space. However, different categories often overlap with each other in the representation space at the beginning of the learning process, which poses a significant challenge for distance-based clustering in achieving good separation between different categories. To this end, we propose Supporting Clustering with Contrastive Learning (SCCL) – a novel framework to leverage contrastive learning to promote better separation. We assess the performance of SCCL on short text clustering and show that SCCL significantly advances the state-of-the-art results on most benchmark datasets with 3%--11% improvement on Accuracy and 4% -- 15% improvement on Normalized Mutual Information. Furthermore, our quantitative analysis demonstrates the effectiveness

Supporting Clustering with Contrastive Learning

**Category:** Cutting-edge **Speakers:** Hung-yi Lee, Abdelrahman Mohamed, Shinji Watanabe, Tara Sainath, Karen Livescu, Shang-Wen Li, Shu-wen Yang, Katrin Kirchhoff **[Speaker Bios](https://2022.naacl.org/program/tutorials/bios-t2/)** **Description:** Although Deep Learning models have revolutionized the speech and audio processing field, they forced building specialist models for individual tasks and application scenarios. Deep neural models also bottlenecked dialects and languages with limited labeled data. Self-supervised representation learning methods promise a single universal model to benefit a collection of tasks and domains. They recently succeeded in NLP and computer vision domains, reaching new performance levels while reducing required labels for many downstream scenarios. Speech representation learning is experiencing similar progress with three main categories: generative, contrastive, predictive. Other approaches relied on multi-modal data for pre-training, mixing text or visual data streams with speech. Although self-supervised speech representation is still a nascent research area, it is closely related to acoustic word embedding and learning with zero lexical resources. This tutorial session will present self-supervised speech representation learning approaches and their connection to related research areas. Since many of the current methods focused solely on automatic speech recognition as a downstream task, we will review recent efforts on benchmarking learned representations to extend the application of such representations beyond speech recognition. A hands-on component of this tutorial will provide practical guidance on building and evaluating speech representation models. **[Please visit our website (with captioned video)](https://sites.google.com/view/tutorial-ssl-speech)**

T2: Self-supervised Representation Learning for Speech Processing 

tutorial

## 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/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

The live sessions will take place in Zoom. Please click on the button below to join a session. 
Plenary room/ paper presentation room 1
[![](https://assets.underline.io/uploads/markdown_image/1/image/a30e692a18780a229ad899bd366cbe7e.png)](https://zoom.us/j/97033720078?pwd=QTcyd3FCcTZNb01BeW5hR2JFR3NLUT09) 

Paper presentation room 2
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Paper presentation room 3
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**Date: ** 6 August 2021 

The live poster session will happen in Gather.town. Please click on the Gather.town button and then navigate to the Workshop poster session room and then to the room associated with this workshop. 

[![](https://assets.underline.io/uploads/markdown_image/1/image/95c65ae2f7fa32b24bb6d9e0980fd96a.png)](https://gather.town/app/T5x469zIFF3Nkfpe/ACL-IJCNLP) 

**Workshop Link: ** https://www.workshopononlineabuse.com/home 

**Schedule: ** 13:00 UTC - 22:00 UTC

The 5th Workshop on Online Abuse and Harms

workshop paper

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

**Reminder:** It is ACL’s policy that at least one author of each accepted paper (including ACL Finding papers) must register for the conference.

**Reminder2:** Underline site will open closer to the event. If you already registered you will receive access detail

Registration is now open

IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

**Session Chair: ** Arman Cohan

15B-Oral: Machine Learning for NLP: Classification and Structured Prediction Models

technical paper

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

**Whova App** 
Stay in touch with your fellow conference attendees via the [Whova App](https://whova.com/portal/webapp/nacon_202106/)

**Conference Structure**
https://2021.naacl.org/blog/conference-structure/

**Walkthrough video of how to NAACL 2021** 

Please take a moment to view this video explaining how to navigate the platform, attend sessions network with other attendees. 


<figure class="video_container">
 <iframe src="https://screencast-o-matic.com/watch/crhwbGVh3vx?v=6&ff=1&title=0&controls=1" width=640 height=350 frameborder="0" allowfullscreen="true"> </iframe>
</figure>

Shang-Wen Li

3

17

Presentations

Self-supervised Representation Learning for Speech Processing

Mitigating Biases in Toxic Language Detection through Invariant Rationalization

Supporting Clustering with Contrastive Learning

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