1 research outputs found
INSPIRED: Toward Sociable Recommendation Dialog Systems
In recommendation dialogs, humans commonly disclose their preference and make
recommendations in a friendly manner. However, this is a challenge when
developing a sociable recommendation dialog system, due to the lack of dialog
dataset annotated with such sociable strategies. Therefore, we present
INSPIRED, a new dataset of 1,001 human-human dialogs for movie recommendation
with measures for successful recommendations. To better understand how humans
make recommendations in communication, we design an annotation scheme related
to recommendation strategies based on social science theories and annotate
these dialogs. Our analysis shows that sociable recommendation strategies, such
as sharing personal opinions or communicating with encouragement, more
frequently lead to successful recommendations. Based on our dataset, we train
end-to-end recommendation dialog systems with and without our strategy labels.
In both automatic and human evaluation, our model with strategy incorporation
outperforms the baseline model. This work is a first step for building sociable
recommendation dialog systems with a basis of social science theories.Comment: Accepted as a long paper at EMNLP 2020, corrected typo