  In retrieval-based dialogue systems, a response selection model acts as a
ranker to select the most appropriate response among several candidates.
However, such selection models tend to rely on context-response content
similarity, which makes models vulnerable to adversarial responses that are
semantically similar but not relevant to the dialogue context. Recent studies
have shown that leveraging these adversarial responses as negative training
samples is useful for improving the discriminating power of the selection
model. Nevertheless, collecting human-written adversarial responses is
expensive, and existing synthesizing methods often have limited scalability. To
overcome these limitations, this paper proposes a simple but efficient method
for generating adversarial negative responses leveraging a large-scale language
model. Experimental results on dialogue selection tasks show that our method
outperforms other methods of synthesizing adversarial negative responses. These
results suggest that our method can be an effective alternative to human
annotators in generating adversarial responses. Our dataset and generation code
is available at https://github.com/leenw23/generating-negatives-by-gpt3.
