NLP datasets are richer than just input-output pairs; rather, they carry
causal relations between the input and output variables. In this work, we take
sentiment classification as an example and look into the causal relations
between the review (X) and sentiment (Y). As psychology studies show that
language can affect emotion, different psychological processes are evoked when
a person first makes a rating and then self-rationalizes their feeling in a
review (where the sentiment causes the review, i.e., Y -> X), versus first
describes their experience, and weighs the pros and cons to give a final rating
(where the review causes the sentiment, i.e., X -> Y ). Furthermore, it is also
a completely different psychological process if an annotator infers the
original rating of the user by theory of mind (ToM) (where the review causes
the rating, i.e., X -ToM-> Y ). In this paper, we verbalize these three causal
mechanisms of human psychological processes of sentiment classification into
three different causal prompts, and study (1) how differently they perform, and
(2) what nature of sentiment classification data leads to agreement or
diversity in the model responses elicited by the prompts. We suggest future
work raise awareness of different causal structures in NLP tasks. Our code and
data are at this https URL

该研究提出了三种不同的因果提示，研究了情感分类数据的不同性质在模型响应中引发的协议的一致性或多样性，并建议未来的研究提高对 NLP 任务中不同因果结构的认识。