Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection (2021)

Abstract: The existence of multiple datasets for sarcasm detection prompts us to apply transfer learning to exploit their commonality. The adversarial neural transfer (ANT) framework utilizes multiple loss terms that encourage the source-domain and the target-domain feature distributions to be similar while optimizing for domain-specific performance. However, these objectives may be in conflict, which can lead to optimization difficulties and sometimes diminished transfer. We propose a generalized latent optimization strategy that allows different losses to accommodate each other and improves training dynamics. The proposed method outperforms transfer learning and meta-learning baselines. In particular, we achieve 10.02% absolute performance gain over the previous state of the art on the iSarcasm dataset.

## introduction sarcastic language is commonly found in social media posts @xcite , forum discussions @xcite , product reviews @xcite and everyday conversations @xcite . detecting sarcasm is an integral part of creative language understanding @xcite and online opinion mining @xcite . due to highly contextualized expressions, detecting sarcasm is a challenging task, even for humans @xcite . a challenge specific to sarcasm detection is the difficulty in acquiring ground-truth annotations. human-annotated datasets @xcite @xcite usually contain only a few thousand texts, resulting in many small datasets. in comparison, automatic data collection using distant supervision signals like hashtags @xcite @xcite yielded * corresponding authors substantially larger datasets. nevertheless, the automatic approach also led to label noise. for example, @xcite found nearly half of the tweets with sarcasm hashtags in one dataset are not sarcastic. the existence of diverse datasets and data collection methods prompts us to exploit their commonality through transfer learning. specifically, we transfer knowledge learned from large and noisy datasets to improve sarcasm detection on small human-annotated datasets that serve as effective performance benchmarks. adversarial neural transfer (ant) @xcite @xcite employs an adversarial setup where the network learns to make the shared feature distributions of the source domain and the target domain as similar as possible, while simultaneously optimizing for domain-specific performance. however, as the domain-specific losses promote the use of domain-specific features, these training objectives may compete with each other implicitly. this leads to optimization difficulties and potentially degenerate cases where the domain-specific classifiers ignore the shared features and no meaningful transfer occurs between domains. to cope with this issue, we propose latent-optimized adversarial neural transfer (loant). the latent optimization strategy can be understood with analogies to to one-step look-ahead during gradient descent and model-agnostic meta learning @xcite . by forcing domain-specific losses to accommodate the negative domain discrimination loss, it improves training dynamics @xcite . with loant, we achieve 10.02% absolute improvement over the previous state of the art on the isarcasm dataset @xcite and 3.08% improvement on semeval-18 dataset @xcite . over four sets of transfer learning experiments, latent optimization on aver-age brings 3.42% improvement in f-score over traditional adversarial neural transfer and 4.83% over a similar training strategy from model-agnostic meta learning (maml) @xcite . in contrast, traditional ant brings an average of only 0.9% f-score improvement over non-adversarial multi-task learning. the results demonstrates that loant can effectively perform knowledge transfer for the task of sarcasm detection and suggests that the proposed latent optimization strategy enables the collaboration among the ant losses during optimization. our contributions can be summarized as follows: to the best of our knowledge, this is the first study of transfer learning between different sarcasm detection datasets. 2. we propose loant, a novel latent-optimized adversarial neural transfer model for crossdomain sarcasm detection. by conducting stochastic gradient descent (sgd) with onestep look-ahead, loant outperforms traditional adversarial neural transfer, multitask learning, and meta-learning baselines, and establishes a new state-of-the-art fscore of 46.41%. the code and datasets are available at https://github.com/ guoxuxu/loant . ## the loant method in supervised transfer learning, we assume labeled data for both the source domain and the target domain are available. the source domain dataset d s comprises of data points in the format of (x s , y s ) and the target domain dataset d t comprises of data points in the format of (x t , y t ). the labels y s and y t are one-hot vectors. the task of supervised crossdomain sarcasm detection can be formulated as learning a target-domain function f t (x t ) that predict correct labels for unseen x t . ## conclusion transfer learning holds the promise for the effective utilization of multiple datasets for sarcasm detection. in this paper, we propose a latent optimization (lo) strategy for adversarial transfer learning for sarcasm detection. by providing look-ahead in the gradient updates, the lo technique allows multiple losses to accommodate each other. this proves to be particularly effective in adversarial transfer learning where the domain-specific losses and the adversarial loss potentially conflict with one another. with the proposed loant method, we set a new state of the art for the isarcasm dataset. we hope the joint utilization of multiple datasets will contribute to the creation of contextualized semantic understanding that is necessary for successful sarcasm detection.