Topic-Aware Causal Intervention for Counterfactual Detection (2024)

Abstract: Counterfactual statements, which describe events that did not or cannot take place, are beneficial to numerous NLP applications. Hence, we consider the problem of counterfactual detection (CFD) and seek to enhance the CFD models. Previous models are reliant on clue phrases to predict counterfactuality, so they suffer from significant performance drop when clue phrase hints do not exist during testing. Moreover, these models tend to predict non-counterfactuals over counterfactuals. To address these issues, we propose to integrate neural topic model into the CFD model to capture the global semantics of the input statement. We continue to causally intervene the hidden representations of the CFD model to balance the effect of the class labels. Extensive experiments show that our approach outperforms previous state-of-the-art CFD and bias-resolving methods in both the CFD and other bias-sensitive tasks.

## introduction counterfactual statements describe an event that may not, did not, or cannot occur, and the consequence(s) that did not occur as well @xcite . for example, consider the statement -i would purchase this physics book, but i really want that my brain has a tiny amount of interest in science!. we can partition the statement into two components: a component about the event (my brain has a tiny amount of interest in science) as the antecedent, and the consequence of the event (i would purchase this physics book) as the consequent. both the antecedent and the consequent did not take place (neither the speaker has purchased the book nor he is interested in science). accurate detection of such counterfactual statements is beneficial to various nlp applications, such as in social media or psychology. in social media, counterfactual detection (cfd) can be helpful by eliminating irrelevant content @xcite . ## methodology in this section, we sequentially formulate the preliminaries of counterfactual detection and neural topic model, introduce our proposed causal perspective for the task, and then articulate the implementation details of our framework. ## conclusion in this paper, we propose a causal intervention framework that discovers biases in the counterfactual detection problem. in order to cope with clue phrase, topic, and label biases, we propose to utilize global semantics and extend the training strategy with deconfounding training objectives. comprehensive experiments demonstrate that our model can ameliorate detrimental influences of biases to polish previous state-of-the-art baselines for not only the counterfactual detection but also other bias-sensitive nlu tasks.