Although deep neural networks have achieved state-of-the-art performance in various machine learning tasks, adversarial examples, constructed by adding small non-random perturbations to correctly classified inputs, successfully fool highly expressive deep classifiers into incorrect predictions. Approaches to adversarial attacks in natural language tasks have boomed in the last five years using character-level, word-level, phrase-level, or sentence-level textual perturbations. While there is some work in NLP on defending against such attacks through proactive methods, like adversarial training, there is to our knowledge no effective general reactive approaches to defence via detection of textual adversarial examples such as is found in the image processing literature. In this paper, we propose two new reactive methods for NLP to fill this gap, which unlike the few limited application baselines from NLP are based entirely on distribution characteristics of learned representations: we adapt one from the image processing literature (Local Intrinsic Dimensionality (LID)), and propose a novel one (MultiDistance Representation Ensemble Method (MDRE)). Adapted LID and MDRE obtain state-of-the-art results on character-level, word-level, and phrase-level attacks on the IMDB dataset as well as on the later two with respect to the MultiNLI dataset. For future research, we publish our code \footnote{Code available at \url{https://github.com/NaLiuAnna/MDRE}}.

Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations

**Organizers:** Spandana Gella, He He, Burcu Can, Maximilian Mozes, Eleonora Giunchiglia, Sewon Min,
Samuel Cahyawijaya, Xiang Lorraine Li and Bodhisattwa Prasad Majumder **Description:** The 7th Workshop on Representation Learning for NLP (RepL4NLP 2022) will be hosted by ACL 2022 and held on
26 May 2022. The workshop is being organised by Spandana Gella, He He, Burcu Can, Maximilian Mozes, Eleonora
Giunchiglia, Sewon Min, Samuel Cahyawijaya, Xiang Lorraine Li and Bodhisattwa Prasad Majumder; and advised by
Isabelle Augenstein, Anna Rogers, Kyunghyun Cho, Edward Grefenstette, Chris Dyer and Laura Rimell. The workshop
is organised by the ACL Special Interest Group on Representation Learning (SIGREP). The 7th Workshop on Representation Learning for NLP aims to continue the success of the Repl4NLP workshop series, with the 1st Workshop
on Representation Learning for NLP having received about 50 submissions and over 250 attendees – the second most
attended collocated event at ACL’16 after WMT). The workshop was introduced as a synthesis of several years of independent ACL workshops focusing on vector space models of meaning, compositionality, and the application of deep
neural networks and spectral methods to NLP. It provides a forum for discussing recent advances on these topics, as well
as future research directions in linguistically motivated vector-based models in NLP. The workshop will take place in a
hybrid setting, and, as in previous years, feature interdisciplinary keynotes, paper presentations, posters, as well as a panel
discussion. 
**Please visit our [website](https://sites.google.com/view/repl4nlp2022/)**

(RepL4NLP)        7th Workshop on Representation Learning for NLP

workshop paper

# Welcome everyone to ACL 2022!

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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). 

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Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations

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