Psychologically-Inspired Causal Prompts

by   Zhiheng Lyu, et al.

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


SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation

Recent studies in recommender systems have managed to achieve significan...

Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews

This paper proposes a new HDP based online review rating regression mode...

DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models

We introduce DoWhy-GCM, an extension of the DoWhy Python library, that l...

ConTrip: Consensus Sentiment review Analysis and Platform ratings in a single score

People unequivocally employ reviews to decide on purchasing an item or a...

Recognizing Emotion Cause in Conversations

Recognizing the cause behind emotions in text is a fundamental yet under...

Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning in NLP

The principle of independent causal mechanisms (ICM) states that generat...

Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation

Review rating prediction of text reviews is a rapidly growing technology...

Please sign up or login with your details

Forgot password? Click here to reset