Zero-shot stance detection (ZSSD) aims to determine whether the author of a text is in support, against, or neutral toward a target that is unseen during training.In this paper, we investigate ZSSD within a bilingual framework and compare it with cross-lingual and monolingual scenarios, in settings that have not previously been explored.Our study focuses on both noun-phrase and claim targets within indomain and out-of-domain bilingual ZSSD scenarios.To support this research, we assemble Bi-STANCE, a comprehensive bilingual ZSSD dataset consisting of over 100,000 annotated text-target pairs in both Chinese and English, sourced from existing datasets.Additionally, we examine a more challenging aspect of bilingual ZSSD by focusing on claim targets with a low occurrence of shared words with their corresponding texts.As part of Bi-STANCE, we created an extended dataset that emphasizes this challenging scenario.To the best of our knowledge, we are the first to explore this difficult ZSSD setting.We investigate these tasks using state-of-the-art pre-trained language models (PLMs) and large language models (LLMs).We release our dataset and code at https://github.com/chenyez/BiSTANCE.

Zero-shot stance detection (ZSSD) aims to determine whether the author of a text is in favor, against, or neutral toward a target that is unseen during training.In this paper, we present EZ-STANCE, a large English ZSSD dataset with 47,316 annotated text-target pairs.In contrast to VAST (Allaway and McKeown, 2020), which is the only other large existing ZSSD dataset for English, EZ-STANCE is 2.5 times larger, includes both noun-phrase targets and claim targets that cover a wide range of domains, provides two challenging subtasks for ZSSD: target-based ZSSD and domain-based ZSSD, and contains much harder examples for the neutral class.We evaluate EZ-STANCE using state-of-the-art deep learning models.Furthermore, we propose to transform ZSSD into the NLI task by applying simple yet effective prompts to noun-phrase targets.Our experimental results show that EZ-STANCE is a challenging new benchmark, which provides significant research opportunities on English ZSSD.

Shweta Yadav, Cornelia Caragea, Chenye Zhao, Naincy Kumari, Marvin Solberg, Tanmay Sharma. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Zero-shot stance detection (ZSSD) aims to determine whether the author of a text is in favor of, against, or neutral toward a target that is unseen during training.Despite the growing attention on ZSSD, most recent advances in this task are limited to English and do not pay much attention to other languages such as Chinese.To support ZSSD research, in this paper, we present C-STANCE that, to our knowledge, is the first Chinese dataset for zero-shot stance detection.We introduce two challenging subtasks for ZSSD: target-based ZSSD and domain-based ZSSD.Our dataset includes both noun-phrase targets and claim targets, covering a wide range of domains.We provide a detailed description and analysis of our dataset.To establish results on C-STANCE, we report performance scores using state-of-the-art deep learning models.We publicly release our dataset and code to facilitate future research.

The goal of zero-shot stance detection (ZSSD) is to identify the stance (in favor of, against, or neutral) of a text towards an unseen target in the inference stage. In this paper, we explore this problem from a novel angle by proposing a Target-based Teacher-Student learning (TTS) framework. Specifically, we first augment the training set by extracting diversified targets that are unseen during training with a keyphrase generation model. Then, we develop a teacher-student framework which effectively utilizes the augmented data. Extensive experiments show that our model significantly outperforms state-of-the-art ZSSD baselines on the available benchmark dataset for this task by 8.9% in macro-averaged F1. In addition, previous ZSSD requires human-annotated targets and labels during training, which may not be available in real-world applications. Therefore, we go one step further by proposing a more challenging open-world ZSSD task: identifying the stance of a text towards an unseen target without human-annotated targets and stance labels. We show that our TTS can be easily adapted to the new task. Remarkably, TTS without human-annotated targets and stance labels even significantly outperforms previous state-of-the-art ZSSD baselines trained with human-annotated data. We publicly release our code 1 to facilitate future research.

With the rapid development of technologies in mobile devices, people can post their daily lives on social networking sites such as Facebook, Flickr, and Instagram. This leads to new privacy concerns due to people’s lack of understanding that private information can be leaked and used to their detriment. Image privacy prediction models are developed to predict whether images contain sensitive information (private images) or are safe to be shared online (public images). Despite significant progress on this task, there are still some crucial problems that remain to be solved. Firstly, images’ content and tags are found to be useful modalities to automatically predict images’ privacy. To date, most image privacy prediction models use single modalities (image-only or tag-only), which limits their performance. Secondly, we observe that current image privacy prediction models are surprisingly vulnerable to even small perturbations in the input data. Attackers can add small perturbations to input data and easily damage a well-trained image privacy prediction model. To address these challenges, in this article, we propose a new decision-level Gated multi-modal fusion (GMMF) approach that fuses object, scene, and image tags modalities to predict privacy for online images. In particular, the proposed approach identifies fusion weights of class probability distributions generated by single-modal classifiers according to their reliability of the privacy prediction for each target image in a sample-by-sample manner and performs a weighted decision-level fusion, so that modalities with high reliability are assigned with higher fusion weights while ones with low reliability are restrained with lower fusion weights. The results of our experiments show that the gated multi-modal fusion network effectively fuses single modalities and outperforms state-of-the-art models for image privacy prediction. Moreover, we perform adversarial training on our proposed GMMF model using multiple types of noise on input data (i.e., images and/or tags). When some modalities are failed by input data with noise attacks, our approach effectively utilizes clean modalities and minimizes negative influences brought by degraded ones using fusion weights, achieving significantly stronger robustness over traditional fusion methods for image privacy prediction. The robustness of our GMMF model against data noise can even be generalized to more severe noise levels. To the best of our knowledge, we are the first to investigate the robustness of image privacy prediction models against noise attacks. Moreover, as the performance of decision-level multi-modal fusion depends highly on the quality of single-modal networks, we investigate self-distillation on single-modal privacy classifiers and observe that transferring knowledge from a trained teacher model to a student model is beneficial in our proposed approach.

Stance detection determines whether the author of a text is in favor of, against or neutral to a specific target and provides valuable insights into important events such as legalization of abortion.Despite significant progress on this task, one of the remaining challenges is the scarcity of annotations.Besides, most previous works focused on a hardlabel training in which meaningful similarities among categories are discarded during training.To address these challenges, first, we evaluate a multi-target and a multi-dataset training settings by training one model on each dataset and datasets of different domains, respectively.We show that models can learn more universal representations with respect to targets in these settings.Second, we investigate the knowledge distillation in stance detection and observe that transferring knowledge from a teacher model to a student model can be beneficial in our proposed training settings.Moreover, we propose an Adaptive Knowledge Distillation (AKD) method that applies instance-specific temperature scaling to the teacher and student predictions.Results show that the multi-dataset model performs best on all datasets and it can be further improved by the proposed AKD, outperforming the state-of-the-art by a large margin.We publicly release our code.1