Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences

Yi Tay, Donovan Ong, Jie Fu, Alvin Chan, Nancy Chen, Anh Tuan Luu, Chris Pal


Abstract
Understanding human preferences, along with cultural and social nuances, lives at the heart of natural language understanding. Concretely, we present a new task and corpus for learning alignments between machine and human preferences. Our newly introduced problem is concerned with predicting the preferable options from two sentences describing scenarios that may involve social and cultural situations. Our problem is framed as a natural language inference task with crowd-sourced preference votes by human players, obtained from a gamified voting platform. We benchmark several state-of-the-art neural models, along with BERT and friends on this task. Our experimental results show that current state-of-the-art NLP models still leave much room for improvement.
Anthology ID:
2020.acl-main.477
Original:
2020.acl-main.477v1
Version 2:
2020.acl-main.477v2
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5369–5373
Language:
URL:
https://aclanthology.org/2020.acl-main.477
DOI:
10.18653/v1/2020.acl-main.477
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.477.pdf
Video:
 http://slideslive.com/38929305