  While improving neural dialogue agents' factual accuracy is the object of
much research, another important aspect of communication, less studied in the
setting of neural dialogue, is transparency about ignorance. In this work, we
analyze to what extent state-of-the-art chit-chat models are linguistically
calibrated in the sense that their verbalized expression of doubt (or
confidence) matches the likelihood that the model's responses are factually
incorrect (or correct). We find that these models are poorly calibrated, yet we
show that likelihood of correctness can accurately be predicted. By
incorporating such metacognitive features into the training of a controllable
generation model, we obtain a dialogue agent with greatly improved linguistic
calibration. While improving neural dialogue agents' factual accuracy is the
object of much research, another important aspect of communication, less
studied in the setting of neural dialogue, is transparency about ignorance. In
this work, we analyze to what extent state-of-the-art chit-chat models are
linguistically calibrated in the sense that their verbalized expression of
doubt (or confidence) matches the likelihood that the model's responses are
factually incorrect (or correct). We find that these models are poorly
calibrated, yet we show that likelihood of correctness can accurately be
predicted. By incorporating such metacognitive features into the training of a
controllable generation model, we obtain a dialogue agent with greatly improved
linguistic calibration.
