@inproceedings{liu-etal-2022-wax,
title = "{WAX}: A New Dataset for Word Association e{X}planations",
author = "Liu, Chunhua and
Cohn, Trevor and
Deyne, Simon De and
Frermann, Lea",
editor = "He, Yulan and
Ji, Heng and
Li, Sujian and
Liu, Yang and
Chang, Chua-Hui",
booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-main.9",
pages = "106--120",
abstract = "Word associations are among the most common paradigms to study the human mental lexicon. While their structure and types of associations have been well studied, surprisingly little attention has been given to the question of why participants produce the observed associations. Answering this question would not only advance understanding of human cognition, but could also aid machines in learning and representing basic commonsense knowledge. This paper introduces a large, crowd-sourced data set of English word associations with explanations, labeled with high-level relation types. We present an analysis of the provided explanations, and design several tasks to probe to what extent current pre-trained language models capture the underlying relations. Our experiments show that models struggle to capture the diversity of human associations, suggesting WAX is a rich benchmark for commonsense modeling and generation.",
}
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<abstract>Word associations are among the most common paradigms to study the human mental lexicon. While their structure and types of associations have been well studied, surprisingly little attention has been given to the question of why participants produce the observed associations. Answering this question would not only advance understanding of human cognition, but could also aid machines in learning and representing basic commonsense knowledge. This paper introduces a large, crowd-sourced data set of English word associations with explanations, labeled with high-level relation types. We present an analysis of the provided explanations, and design several tasks to probe to what extent current pre-trained language models capture the underlying relations. Our experiments show that models struggle to capture the diversity of human associations, suggesting WAX is a rich benchmark for commonsense modeling and generation.</abstract>
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%0 Conference Proceedings
%T WAX: A New Dataset for Word Association eXplanations
%A Liu, Chunhua
%A Cohn, Trevor
%A Deyne, Simon De
%A Frermann, Lea
%Y He, Yulan
%Y Ji, Heng
%Y Li, Sujian
%Y Liu, Yang
%Y Chang, Chua-Hui
%S Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online only
%F liu-etal-2022-wax
%X Word associations are among the most common paradigms to study the human mental lexicon. While their structure and types of associations have been well studied, surprisingly little attention has been given to the question of why participants produce the observed associations. Answering this question would not only advance understanding of human cognition, but could also aid machines in learning and representing basic commonsense knowledge. This paper introduces a large, crowd-sourced data set of English word associations with explanations, labeled with high-level relation types. We present an analysis of the provided explanations, and design several tasks to probe to what extent current pre-trained language models capture the underlying relations. Our experiments show that models struggle to capture the diversity of human associations, suggesting WAX is a rich benchmark for commonsense modeling and generation.
%U https://aclanthology.org/2022.aacl-main.9
%P 106-120
Markdown (Informal)
[WAX: A New Dataset for Word Association eXplanations](https://aclanthology.org/2022.aacl-main.9) (Liu et al., AACL-IJCNLP 2022)
ACL
- Chunhua Liu, Trevor Cohn, Simon De Deyne, and Lea Frermann. 2022. WAX: A New Dataset for Word Association eXplanations. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 106–120, Online only. Association for Computational Linguistics.