@inproceedings{tay-etal-2020-rather,
title = "Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences",
author = "Tay, Yi and
Ong, Donovan and
Fu, Jie and
Chan, Alvin and
Chen, Nancy and
Luu, Anh Tuan and
Pal, Chris",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.477",
doi = "10.18653/v1/2020.acl-main.477",
pages = "5369--5373",
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.",
}
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%0 Conference Proceedings
%T Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences
%A Tay, Yi
%A Ong, Donovan
%A Fu, Jie
%A Chan, Alvin
%A Chen, Nancy
%A Luu, Anh Tuan
%A Pal, Chris
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F tay-etal-2020-rather
%X 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.
%R 10.18653/v1/2020.acl-main.477
%U https://aclanthology.org/2020.acl-main.477
%U https://doi.org/10.18653/v1/2020.acl-main.477
%P 5369-5373
Markdown (Informal)
[Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences](https://aclanthology.org/2020.acl-main.477) (Tay et al., ACL 2020)
ACL