We address the task of identifying euphemistic abuse (e.g. "You inspire me to fall asleep") paraphrasing simple explicitly abusive utterances (e.g. "You are boring"). For this task, we introduce a novel dataset that has been created via crowdsourcing. Special attention has been paid to the generation of appropriate negative (non-abusive) data. We report on classification experiments showing that classifiers trained on previous datasets are less capable of detecting such abuse. Best automatic results are obtained by a classifier that augments training data from our new dataset with automatically-generated GPT-3 completions. We also present a classifier that combines a few manually extracted features that exemplify the major linguistic phenomena constituting euphemistic abuse.

Euphemistic Abuse – A New Dataset and Classification Experiments for Implicitly Abusive Language | VIDEO

Accounting for different degrees of formality is crucial for producing contextually appropriate language. To assist NLP applications concerned with this problem and formality analysis in general, we present the first dataset of sentences from a wide range of genres assessed on a continuous informal-formal scale via comparative judgments. It is the first corpus with a comprehensive perspective on German sentence-level formality overall. We compare machine learning models for formality scoring, a task we treat as a regression problem, on our dataset. Finally, we investigate the relation between sentence- and document-level formality and evaluate leveraging sentence-based annotations for assessing formality on documents.

A Question of Style: A Dataset for Analyzing Formality on Different Levels

We address the task of distinguishing implicitly abusive sentences on identity groups
("Muslims contaminate our planet") from other group-related negative polar sentences ("Muslims despise terrorism"). Implicitly abusive language are utterances not conveyed by abusive words (e.g. "bimbo" or "scum"). So far, the detection of such
utterances could not be properly addressed since existing datasets displaying a high
degree of implicit abuse are fairly biased. Following the recently-proposed strategy to solve implicit abuse by separately addressing its different subtypes, we present a new focused and less biased dataset that consists of the subtype of atomic negative
sentences about identity groups. For that task, we model components that each
address one facet of such implicit abuse, i.e. depiction as perpetrators, aspectual
classification and non-conformist views. The approach generalizes across different
identity groups and languages.

Identifying Implicitly Abusive Remarks about Identity Groups using a Linguistically Informed Approach

Abusive language detection is an emerging field in natural language processing which has received a large amount of attention recently. Still the success of automatic detection is limited. Particularly, the detection of implicitly abusive language, i.e. abusive language that is not conveyed by abusive words (e.g. dumbass or scum), is not working well. In this position paper, we explain why existing datasets make learning implicit abuse difficult and what needs to be changed in the design of such datasets. Arguing for a divide-and-conquer strategy, we present a list of subtypes of implicitly abusive language and formulate research tasks and questions for future research.

Implicitly Abusive Language -- What does it actually look like and why are we not getting there?

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**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

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Findings

findings / work in progress

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

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EACL 2023

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

3B: Semantics & Sentiment Analysis

technical paper

## 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! 
 
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[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

**Session Chair: ** Ahmed Awadallah

2D-Oral: Special Theme: New Challenges in NLP

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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Elisabeth Eder

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SHORT BIO

Presentations

Euphemistic Abuse – A New Dataset and Classification Experiments for Implicitly Abusive Language | VIDEO

A Question of Style: A Dataset for Analyzing Formality on Different Levels

Identifying Implicitly Abusive Remarks about Identity Groups using a Linguistically Informed Approach

Implicitly Abusive Language -- What does it actually look like and why are we not getting there?

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PRESENTATIONS

CONFERENCES

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