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Found 34 papers, 6 papers with code
Change My Mind: How Syntax-based Hate Speech Recognizer Can Uncover Hidden Motivations Based on Different Viewpoints
Hate speech recognizers may mislabel sentences by not considering the different opinions that society has on selected topics.
We Need to Consider Disagreement in Evaluation
Instead, we suggest that we need to better capture the sources of disagreement to improve today’s evaluation practice.
Italian NLP for Everyone: Resources and Models from EVALITA to the European Language Grid
The European Language Grid enables researchers and practitioners to easily distribute and use NLP resources and models, such as corpora and classifiers.
HurtBERT: Incorporating Lexical Features with BERT for the Detection of Abusive Language
The detection of abusive or offensive remarks in social texts has received significant attention in research.
Counter-TWIT: An Italian Corpus for Online Counterspeech in Ecological Contexts
This work describes the process of creating a corpus of Twitter conversations annotated for the presence of counterspeech in response to toxic speech related to axes of discrimination linked to sexism, racism and homophobia.
Litescale: A Lightweight Tool for Best-worst Scaling Annotation
Best-worst Scaling (BWS) is a methodology for annotation based on comparing and ranking instances, rather than classifying or scoring individual instances.
Capturing Perspectives of Crowdsourced Annotators in Subjective Learning Tasks
Previous studies have shed light on the pitfalls of label aggregation and have introduced a handful of practical approaches to tackle this issue.
SemEval-2023 Task 11: Learning With Disagreements (LeWiDi)
We report on the second LeWiDi shared task, which differs from the first edition in three crucial respects: (i) it focuses entirely on NLP, instead of both NLP and computer vision tasks in its first edition; (ii) it focuses on subjective tasks, instead of covering different types of disagreements-as training with aggregated labels for subjective NLP tasks is a particularly obvious misrepresentation of the data; and (iii) for the evaluation, we concentrate on soft approaches to evaluation.
APPReddit: a Corpus of Reddit Posts Annotated for Appraisal
Despite the large number of computational resources for emotion recognition, there is a lack of data sets relying on appraisal models.
Local Multi-Head Channel Self-Attention for Facial Expression Recognition
Since the Transformer architecture was introduced in 2017 there has been many attempts to bring the self-attention paradigm in the field of computer vision.
Ranked #6 on
Facial Expression Recognition (FER)
on FER2013
(using extra training data)
Facial Expression Recognition
Facial Expression Recognition (FER)
Toward a Perspectivist Turn in Ground Truthing for Predictive Computing
Most Artificial Intelligence applications are based on supervised machine learning (ML), which ultimately grounds on manually annotated data.
Whose Opinions Matter? Perspective-aware Models to Identify Opinions of Hate Speech Victims in Abusive Language Detection
Therefore, we need novel approaches to model conflicting perspectives and opinions coming from people with different personal and demographic backgrounds.
GruPaTo at SemEval-2020 Task 12: Retraining mBERT on Social Media and Fine-tuned Offensive Language Models
We introduce an approach to multilingual Offensive Language Detection based on the mBERT transformer model.
Multilingual Irony Detection with Dependency Syntax and Neural Models
This paper presents an in-depth investigation of the effectiveness of dependency-based syntactic features on the irony detection task in a multilingual perspective (English, Spanish, French and Italian).
HateBERT: Retraining BERT for Abusive Language Detection in English
In this paper, we introduce HateBERT, a re-trained BERT model for abusive language detection in English.
Ranked #1 on
Hate Speech Detection
on AbusEval
I Feel Offended, Don't Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language
However, there is a lack of data sets that take into account the degree of explicitness.
Do You Really Want to Hurt Me? Predicting Abusive Swearing in Social Media
In this study, we explore the phenomenon of swearing in Twitter conversations, taking the possibility of predicting the abusiveness of a swear word in a tweet context as the main investigation perspective.
FlorUniTo@TRAC-2: Retrofitting Word Embeddings on an Abusive Lexicon for Aggressive Language Detection
This paper describes our participation to the TRAC-2 Shared Tasks on Aggression Identification.
The DipInfoUniTo Realizer at SRST'19: Learning to Rank and Deep Morphology Prediction for Multilingual Surface Realization
We describe the system presented at the SR{'}19 shared task by the DipInfoUnito team.
SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter
The paper describes the organization of the SemEval 2019 Task 5 about the detection of hate speech against immigrants and women in Spanish and English messages extracted from Twitter.
Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure
On this line, a new shared task has been proposed at SemEval-2017 (Task 8, SubTask A), which is focused on rumour stance classification in English tweets.
The DipInfo-UniTo system for SRST 2018
This paper describes the system developed by the DipInfo-UniTo team to participate to the shallow track of the Surface Realization Shared Task 2018.
SemEval 2018 Task 2: Multilingual Emoji Prediction
This paper describes the results of the first Shared Task on Multilingual Emoji Prediction, organized as part of SemEval 2018.
Measuring Frame Instance Relatedness
Frame semantics is a well-established framework to represent the meaning of natural language in computational terms.
Empirical Analysis of Foundational Distinctions in Linked Open Data
For example, distinctions such as whether an entity is inherently a class or an individual, or whether it is a physical object or not, are hardly expressed in the data, although they have been largely studied and formalised by foundational ontologies (e. g. DOLCE, SUMO).
Developing a large semantically annotated corpus
What would be a good method to provide a large collection of semantically annotated texts with formal, deep semantics rather than shallow?
