This paper tackles the task of emotion-cause pair extraction in the unsupervised domain adaptation setting.The problem is challenging as the distributions of the events causing emotions in target domains are dramatically different than those in source domains, despite the distributions of emotional expressions between domains are overlapped. Inspired by causal discovery,we propose a novel deep latent model in the variational autoencoder (VAE) framework, which not only captures the underlying latent structures of data but also utilizes the easily transferable knowledge of emotions as the bridge to link the distributions of events in different domains. To facilitate knowledge transfer across domains, we also propose a novel variational posterior regularization technique to disentangle the latent representations of emotions from those of events in order to mitigate the damage caused by the spurious correlations related to the events in source domains. Through extensive experiments, we demonstrate that our model outperforms the strongest baseline by approximately 11.05% on a Chinese benchmark and 2.45% on a English benchmark in terms of weighted-average F1 score. We have released our source code and the generated dataset publicly at: https://github.com/tk1363704/CAREL-VAE.

Semantic parsing is a technique aimed at constructing a structured representation of the meaning of a natural-language question. Recent advances in language models trained on code have shown superior performance in generating these representations compared to language models trained solely on natural language text. The existing fine-tuned neural semantic parsers are vulnerable to adversarial attacks on natural-language inputs. While it has been established that the robustness of smaller semantic parsers can be enhanced through adversarial training, this approach is not feasible for large language models in real-world scenarios, as it requires both substantial computational resources and expensive human annotation on in-domain semantic parsing data. This paper presents the first empirical study on the adversarial robustness of a prompt-based semantic parser based on CODEX, a stateof-the-art (SOTA) language model trained on code. Our results demonstrate that the large language model of code is vulnerable to carefully crafted adversarial examples. To overcome this challenge, we propose methods for enhancing robustness without requiring substantial amounts of labelled data or intensive computational resources.