INSPIRED: Toward Sociable Recommendation Dialog Systems

Shirley Anugrah Hayati, Dongyeop Kang, Qingxiaoyang Zhu, Weiyan Shi, Zhou Yu


Abstract
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.
Anthology ID:
2020.emnlp-main.654
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8142–8152
Language:
URL:
https://aclanthology.org/2020.emnlp-main.654
DOI:
10.18653/v1/2020.emnlp-main.654
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.654.pdf
Optional supplementary material:
 2020.emnlp-main.654.OptionalSupplementaryMaterial.zip
Video:
 https://slideslive.com/38939155
Code
 sweetpeach/Inspired
Data
Inspired