Causal inference has shown potential in enhancing the predictive accuracy, fairness,
robustness, and explainability of Natural Language Processing (NLP) models by capturing …

Q&A platforms have been an integral part of the web-help-seeking behavior of programmers
over the past decade. However, with the recent introduction of ChatGPT, the paradigm of …

Q&A platforms have been crucial for the online help-seeking behavior of programmers.
However, the recent popularity of ChatGPT is altering this trend. Despite this popularity, no …

Despite the importance of understanding causality, corpora addressing causal relations are
limited. There is a discrepancy between existing annotation guidelines of event causality …

The ability to perform causal reasoning is widely considered a core feature of intelligence. In
this work, we investigate whether large language models (LLMs) can coherently reason …

Acceptance testing is crucial to determine whether a system fulfills end-user requirements.
However, the creation of acceptance tests is a laborious task entailing two major …

Identifying the emotional causes of mental illnesses is key to effective intervention. Existing
emotion-cause analysis approaches can effectively detect simple emotion-cause …

Stack Overflow (SO) is becoming an indispensable part of modern software development
workflow. However, given the limited time, attention, and memory capacity of programmers …

Causal relations in natural language (NL) requirements convey strong, semantic
information. Automatically extracting such causal information enables multiple use cases …

Scarcity of annotated causal texts leads to poor robustness when training state-of-the-art
language models for causal sentence classification. In particular, we found that models …