  Existing approaches built separate classifiers to detect nonsense in
dialogues. In this paper, we show that without external classifiers, dialogue
models can detect errors in their own messages introspectively, by calculating
the likelihood of replies that are indicative of poor messages. For example, if
an agent believes its partner is likely to respond "I don't understand" to a
candidate message, that message may not make sense, so an alternative message
should be chosen. We evaluate our approach on a dataset from the game
Diplomacy, which contains long dialogues richly grounded in the game state, on
which existing models make many errors. We first show that hand-crafted replies
can be effective for the task of detecting nonsense in applications as complex
as Diplomacy. We then design AutoReply, an algorithm to search for such
discriminative replies automatically, given a small number of annotated
dialogue examples. We find that AutoReply-generated replies outperform
handcrafted replies and perform on par with carefully fine-tuned large
supervised models. Results also show that one single reply without much
computation overheads can also detect dialogue nonsense reasonably well.
