Multilingual spoken language understanding (SLU) consists of two sub-tasks, namely intent detection and slot filling. To improve the performance of these two sub-tasks, we propose to use consistency regularization based on a hybrid data augmentation strategy. The consistency regularization enforces the predicted distributions for an example and its semantically equivalent augmentation to be consistent. We conduct experiments on the MASSIVE dataset under both full-dataset and zero-shot settings. Experimental results demonstrate that our proposed method improves the performance on both intent detection and slot filling tasks. Our system ranked 1st in the MMNLU-22 competition under the full-dataset setting.

HIT-SCIR at MMNLU-22: Consistency Regularization for Multilingual Spoken Language Understanding

The Mixture-of-Experts (MoE) technique can scale up the model size of Transformers with an affordable computational overhead. We point out that existing learning-to-route MoE methods suffer from the routing fluctuation issue, i.e., the target expert of the same input may change along with training, but only one expert will be activated for the input during inference. The routing fluctuation tends to harm sample efficiency because the same input updates different experts but only one is finally used. In this paper, we propose StableMoE with two training stages to address the routing fluctuation problem. In the first training stage, we learn a balanced and cohesive routing strategy and distill it into a lightweight router decoupled from the backbone model. In the second training stage, we utilize the distilled router to determine the token-to-expert assignment and freeze it for a stable routing strategy. We validate our method on language modeling and multilingual machine translation. The results show that StableMoE outperforms existing MoE methods in terms of both convergence speed and performance.

StableMoE: Stable Routing Strategy for Mixture of Experts

In this paper, we introduce ELECTRA-style tasks to cross-lingual language model pre-training. Specifically, we present two pre-training tasks, namely multilingual replaced token detection, and translation replaced token detection. Besides, we pretrain the model, named as XLM-E, on both multilingual and parallel corpora. Our model outperforms the baseline models on various cross-lingual understanding tasks with much less computation cost. Moreover, analysis shows that XLM-E tends to obtain better cross-lingual transferability.

XLM-E: Cross-lingual Language Model Pre-training via ELECTRA

**Chairs:** Chris Hench, Kay Rottmann, Jack FitzGerald and Charith Peris **Organizers:** Jack FitzGerald, Kay Rottmann, Julia Hirschberg, Mohit Bansal, Anna Rumshisky and Charith Peris 
**Invited speakers**: [Heng Ji](https://underline.io/speakers/98606-heng-ji),[ Geraldine Damnati](https://underline.io/speakers/214048-geraldine-damnati), [Mahdi Namazifar](https://underline.io/speakers/197578-mahdi-namazifar), [Anna Rumshisky](https://underline.io/speakers/61205-anna-rumshisky), [Sebastian Ruder](https://underline.io/speakers/99090-sebastian-ruder) and [David Yarowsky](https://underline.io/speakers/56810-david-yarowsky) Please visit our [**website**](https://mmnlu-22.github.io/) for more information and detailed schedule.

**RocketChat channel for this workshop:** https://emnlp2022.rocket.chat/channel/workshop-7-mmnlu

W7 (MMNLU) Massively Multilingual Natural Language Understanding

workshop paper

## Welcome to EMNLP 2022!
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Our key innovations for EMNLP 2022 include:

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***EMNLP 2022 General Chair***
 
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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.

Posters: Machine Learning for NLP

poster

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
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, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 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 will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

Bo Zheng

3

3

SHORT BIO

Presentations

HIT-SCIR at MMNLU-22: Consistency Regularization for Multilingual Spoken Language Understanding

StableMoE: Stable Routing Strategy for Mixture of Experts

XLM-E: Cross-lingual Language Model Pre-training via ELECTRA

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