Human feedback on poor dialog system performance is important for the development of
#ConvAI
. ππ
But datasets with free-text human feedback are scarce. Can data augmentation come to the rescue?
A 𧡠on one of our
#EMNLP2023
papers (1/11).
π°
#NLProc
Our findings provide new insights into the composition of the examined datasets, including error types, user response types, and the relations between them. (2/π§΅)
#EMNLP2023
Learning from free-text human feedback improves user acceptance. However, the lack of available datasets, i.e., annotations for errors in system utterances and following free-text human feedback, hinders research. (3/π§΅)
Recently, data augmentation using synthetic data generation has shown promise, but they usually require the information to be included in the seed data. (4/π§΅)
#EMNLP2023
And it is an open question to what extent existing dialog datasets contain errors in system utterances and free-text human feedback. (5/π§΅)
#EMNLP2023