Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Pre-Trained Language Models for Producing Counter Narratives
  Against Hate Speech: a Comparative Study   In this work, we present an extensive study on the use of pre-trained
language models for the task of automatic Counter Narrative (CN) generation to
fight online hate speech in English. We first present a comparative study to
determine whether there is a particular Language Model (or class of LMs) and a
particular decoding mechanism that are the most appropriate to generate CNs.
Findings show that autoregressive models combined with stochastic decodings are
the most promising. We then investigate how an LM performs in generating a CN
with regard to an unseen target of hate. We find out that a key element for
successful `out of target' experiments is not an overall similarity with the
training data but the presence of a specific subset of training data, i.e. a
target that shares some commonalities with the test target that can be defined
a-priori. We finally introduce the idea of a pipeline based on the addition of
an automatic post-editing step to refine generated CNs.

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The categories of this paper are: q-alg
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The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
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The categories of this paper are: bayes-an
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The categories of this paper are: eess
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
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The categories of this paper are: supr-con
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The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph