We describe our approach and experiments to tackle Task A of the second edition of HaSpeeDe, within the Evalita 2020 evaluation campaign. The proposed model consists in an ensemble of classifiers built from three variants of a common neural architecture. Each classifier uses contextual representations from transformers trained on Italian texts, fine tuned on the training set of the challenge. We tested the proposed model on the two official test sets, the in-domain test set containing just tweets and the out-of-domain one including also news headlines. Our submissions ranked 4th on the tweets test set and 17th on the second test set
Certain events or political situations determine users from the online environment to express themse...
This paper describes the UNITOR system that participated to the Stance Detection in Italian tweets (...
We describe the systems the RuG Team developed in the context of the Hate Speech Detection Task in I...
We describe our approach and experiments to tackle Task A of the second edition of HaSpeeDe, within ...
This report describes an approach to face a task regarding the identification of hate content and st...
We describe our approach to address Task A of the EVALITA 2020 Hate Speech Detection (HaSpeeDe2) cha...
In this article, we present the results of applying a Stacking Ensemble method to the problem of hat...
We describe in this paper the system submitted by the DH-FBK team to the HaSpeeDe evaluation task, a...
This paper explains the system developed for the Hate Speech Detection (HaSpeeDe) shared task within...
This report was written to describe the systems that were submitted by the team “TheNorth” for the H...
International audienceThis paper reports on the systems the InriaFBK Team submitted to the EVALITA 2...
Hate speech detection has become a crucial mission in many fields. This paper introduces the system ...
This paper describes the system that team YNU_OXZ submitted for EVALITA 2020. We participate in the ...
The present paper describes two neural network systems used for Hate Speech Detection tasks that mak...
We describe in this paper the system submitted by the DH-FBK team to the HaSpeeDe evaluation task, a...
Certain events or political situations determine users from the online environment to express themse...
This paper describes the UNITOR system that participated to the Stance Detection in Italian tweets (...
We describe the systems the RuG Team developed in the context of the Hate Speech Detection Task in I...
We describe our approach and experiments to tackle Task A of the second edition of HaSpeeDe, within ...
This report describes an approach to face a task regarding the identification of hate content and st...
We describe our approach to address Task A of the EVALITA 2020 Hate Speech Detection (HaSpeeDe2) cha...
In this article, we present the results of applying a Stacking Ensemble method to the problem of hat...
We describe in this paper the system submitted by the DH-FBK team to the HaSpeeDe evaluation task, a...
This paper explains the system developed for the Hate Speech Detection (HaSpeeDe) shared task within...
This report was written to describe the systems that were submitted by the team “TheNorth” for the H...
International audienceThis paper reports on the systems the InriaFBK Team submitted to the EVALITA 2...
Hate speech detection has become a crucial mission in many fields. This paper introduces the system ...
This paper describes the system that team YNU_OXZ submitted for EVALITA 2020. We participate in the ...
The present paper describes two neural network systems used for Hate Speech Detection tasks that mak...
We describe in this paper the system submitted by the DH-FBK team to the HaSpeeDe evaluation task, a...
Certain events or political situations determine users from the online environment to express themse...
This paper describes the UNITOR system that participated to the Stance Detection in Italian tweets (...
We describe the systems the RuG Team developed in the context of the Hate Speech Detection Task in I...