ReSeTOX: Re-learning attention weights for toxicity mitigation in
  machine translation
Javier García Gilabert Carlos Escolano Marta R. Costa-Jussà
Our proposed method, ReSeTOX (REdo SEarch if TOXic), addresses the issue of
Neural Machine Translation (NMT) generating translation outputs that contain
toxic words not present in the input. The objective is to mitigate the
introduction of toxic language without the need for re-training. In the case of
identified added toxicity during the inference process, ReSeTOX dynamically
adjusts the key-value self-attention weights and re-evaluates the beam search
hypotheses. Experimental results demonstrate that ReSeTOX achieves a remarkable
57% reduction in added toxicity while maintaining an average translation
quality of 99.5% across 164 languages.