  The Counter Narrative (CN) is a promising approach to combat online hate
speech (HS) without infringing on freedom of speech. In recent years, there has
been a growing interest in automatically generating CNs using natural language
generation techniques. However, current automatic CN generation methods mainly
rely on expert-authored datasets for training, which are time-consuming and
labor-intensive to acquire. Furthermore, these methods cannot directly obtain
and extend counter-knowledge from external statistics, facts, or examples. To
address these limitations, we propose Retrieval-Augmented Unsupervised Counter
Narrative Generation (RAUCG) to automatically expand external counter-knowledge
and map it into CNs in an unsupervised paradigm. Specifically, we first
introduce an SSF retrieval method to retrieve counter-knowledge from the
multiple perspectives of stance consistency, semantic overlap rate, and fitness
for HS. Then we design an energy-based decoding mechanism by quantizing
knowledge injection, countering and fluency constraints into differentiable
functions, to enable the model to build mappings from counter-knowledge to CNs
without expert-authored CN data. Lastly, we comprehensively evaluate model
performance in terms of language quality, toxicity, persuasiveness, relevance,
and success rate of countering HS, etc. Experimental results show that RAUCG
outperforms strong baselines on all metrics and exhibits stronger
generalization capabilities, achieving significant improvements of +2.0% in
relevance and +4.5% in success rate of countering metrics. Moreover, RAUCG
enabled GPT2 to outperform T0 in all metrics, despite the latter being
approximately eight times larger than the former. Warning: This paper may
contain offensive or upsetting content!
