  The recognition of hate speech and offensive language (HOF) is commonly
formulated as a classification task to decide if a text contains HOF. We
investigate whether HOF detection can profit by taking into account the
relationships between HOF and similar concepts: (a) HOF is related to sentiment
analysis because hate speech is typically a negative statement and expresses a
negative opinion; (b) it is related to emotion analysis, as expressed hate
points to the author experiencing (or pretending to experience) anger while the
addressees experience (or are intended to experience) fear. (c) Finally, one
constituting element of HOF is the mention of a targeted person or group. On
this basis, we hypothesize that HOF detection shows improvements when being
modeled jointly with these concepts, in a multi-task learning setup. We base
our experiments on existing data sets for each of these concepts (sentiment,
emotion, target of HOF) and evaluate our models as a participant (as team
IMS-SINAI) in the HASOC FIRE 2021 English Subtask 1A. Based on model-selection
experiments in which we consider multiple available resources and submissions
to the shared task, we find that the combination of the CrowdFlower emotion
corpus, the SemEval 2016 Sentiment Corpus, and the OffensEval 2019 target
detection data leads to an F1 =.79 in a multi-head multi-task learning model
based on BERT, in comparison to .7895 of plain BERT. On the HASOC 2019 test
data, this result is more substantial with an increase by 2pp in F1 and a
considerable increase in recall. Across both data sets (2019, 2021), the recall
is particularly increased for the class of HOF (6pp for the 2019 data and 3pp
for the 2021 data), showing that MTL with emotion, sentiment, and target
identification is an appropriate approach for early warning systems that might
be deployed in social media platforms.

Computation and Language