Social media's global reach amplifies the spread of information, highlighting the need for robust Natural Language Processing tasks like stance detection across languages and modalities. Prior research predominantly focuses on text-only inputs, leaving multimodal scenarios, such as those involving both images and text, relatively underexplored. Meanwhile, the prevalence of multimodal posts has increased significantly in recent years. Although state-of-the-art Vision-Language Models (VLMs) show promise, their performance on multimodal and multilingual stance detection tasks remains largely unexamined. This paper evaluates state-of-the-art VLMs on a newly extended dataset covering seven languages and multimodal inputs, investigating their use of visual cues, language-specific performance, and cross-modality interactions. Our results show that VLMs generally rely more on text than images for stance detection and this trend persists across languages. Additionally, VLMs rely significantly more on text contained within the images than other visual content. Regarding multilinguality, the models studied tend to generate consistent predictions across languages whether they are explicitly multilingual or not, although there are outliers that are incongruous with macro F1, language support, and model size.
University of Zurich（苏黎世大学）；University of Edinburgh（爱丁堡大学）
We extract a large-scale stance detection dataset from comments written by candidates of elections in Switzerland. The dataset consists of German, French and Italian text, allowing for a cross-lingual evaluation of stance detection. It contains 67 000 comments on more than 150 political issues (targets). Unlike stance detection models that have specific target issues, we use the dataset to train a single model on all the issues. To make learning across targets possible, we prepend to each instance a natural question that represents the target (e.g. "Do you support X?"). Baseline results from m…
University of the Basque Country UPV/EHU（巴斯克大学（UPV/EHU））；Intercom Strategys（Intercom战略公司）
Stance detection aims to determine the attitude of a given text with respect to a specific topic or claim. While stance detection has been fairly well researched in the last years, most the work has been focused on English. This is mainly due to the relative lack of annotated data in other languages. The TW-10 Referendum Dataset released at IberEval 2018 is a previous effort to provide multilingual stance-annotated data in Catalan and Spanish. Unfortunately, the TW-10 Catalan subset is extremely imbalanced. This paper addresses these issues by presenting a new multilingual dataset for stance d…
Can MLLMs Generalize to Multi-Party dialog? Exploring Multilingual Response Generation in Complex Scenarios
School of Computer Science and Engineering, Northwestern Polytechnical University（计算机科学与工程学院，西北工业大学）；School of Computer Science and Technology, Xidian University（计算机科学与技术学院，西安电子科技大学）；School of Artificial Intelligence and Big Data, Henan University of Technology（人工智能与大数据学院，河南工业大学）
School of Interactive Computing（交互计算学院）；Georgia Institute of Technology（佐治亚理工学院）
We present Stanceosaurus, a new corpus of 28,033 tweets in English, Hindi, and Arabic annotated with stance towards 251 misinformation claims. As far as we are aware, it is the largest corpus annotated with stance towards misinformation claims. The claims in Stanceosaurus originate from 15 fact-checking sources that cover diverse geographical regions and cultures. Unlike existing stance datasets, we introduce a more fine-grained 5-class labeling strategy with additional subcategories to distinguish implicit stance. Pre-trained transformer-based stance classifiers that are fine-tuned on our cor…
Vicomtech Foundation（维康泰克基金会）；Basque Research and Technology Alliance (BRTA)（巴斯克研究与技术联盟（BRTA））；University of the Basque Country UPV/EHU（巴斯克大学（UPV/EHU））
Popular social media networks provide the perfect environment to study the opinions and attitudes expressed by users. While interactions in social media such as Twitter occur in many natural languages, research on stance detection (the position or attitude expressed with respect to a specific topic) within the Natural Language Processing field has largely been done for English. Although some efforts have recently been made to develop annotated data in other languages, there is a telling lack of resources to facilitate multilingual and crosslingual research on stance detection. This is partiall…
DimStance: Multilingual Datasets for Dimensional Stance Analysis
University of Göttingen（弗赖堡大学）；Yuan Ze University（元智大学）；Imperial College London（伦敦帝国理工学院）；University of Pretoria（南非普里蒂亚大学）；National Yang Ming Chiao Tung University；Maseno University（马斯诺大学）；Central Police University Taiwan（台湾中央警察大学）；Yunnan University（云南大学）；Bayero University Kano（卡诺巴耶罗大学）；National Research Council Canada（加拿大国家研究委员会）
Cross-Lingual Consistency: A Novel Inference Framework for Advancing Reasoning in Large Language Models
Shandong Inspur Artificial Intelligence Research Institute Co., Ltd.（山东浪潮人工智能研究院有限公司）；Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Center Co., Ltd.（山东云海国创云计算装备产业创新中心有限公司）
The aim of the CASE 2021 Shared Task 1 (Hürriyetoğlu et al., 2021) was to detect and classify socio-political and crisis event information at document, sentence, cross-sentence, and token levels in a multilingual setting, with each of these subtasks being evaluated separately in each test language. Our submission contained entries in all of the subtasks, and the scores obtained validated our research finding: That the multilingual aspect of the tasks should be embraced, so that modeling and training regimes use the multilingual nature of the tasks to their mutual benefit, rather than trying to…
Queen Mary University of London（伦敦玛丽女王大学）；Imam Abdulrahman bin Faisal University（伊玛目阿卜杜勒拉赫曼·本·费萨尔大学）
This paper presents our submission to the SardiStance 2020 shared task, describing the architecture used for Task A and Task B. While our submission for Task A did not exceed the baseline, retraining our model using all the training tweets, showed promising results leading to (f-avg 0.601) using bidirectional LSTM with BERT multilingual embedding for Task A. For our submission for Task B, we ranked 6th (f-avg 0.709). With further investigation, our best experimented settings increased performance from (f-avg 0.573) to (f-avg 0.733) with same architecture and parameter settings and after only i…
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究所）；Technical University of Munich（慕尼黑工业大学）
Detoxification is a task of generating text in polite style while preserving meaning and fluency of the original toxic text. Existing detoxification methods are designed to work in one exact language. This work investigates multilingual and cross-lingual detoxification and the behavior of large multilingual models like in this setting. Unlike previous works we aim to make large language models able to perform detoxification without direct fine-tuning in given language. Experiments show that multilingual models are capable of performing multilingual style transfer. However, models are not able …
FarExStance: Explainable Stance Detection for Farsi
Dublin City University（都柏林城市大学）；Iran University of Science and Technology（伊朗科技大学）；Dadmatech（Dadmatech公司）；University of Birmingham（伯明翰大学）；Cardiff University（卡迪夫大学）
Rationale-Guided Knowledge Distillation for Cross-Lingual Stance Detection
School of Foreign Studies, Hefei University of Technology（合肥工业大学外国语学院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）；Hefei Institute of Technology（合肥学院）
Checkstep Research（Checkstep 研究）；University of Copenhagen（哥本哈根大学）；Qatar Computing Research Institute, HBKU（卡塔尔计算研究所，哈马德·本·哈利法大学）
The goal of stance detection is to determine the viewpoint expressed in a piece of text towards a target. These viewpoints or contexts are often expressed in many different languages depending on the user and the platform, which can be a local news outlet, a social media platform, a news forum, etc. Most research in stance detection, however, has been limited to working with a single language and on a few limited targets, with little work on cross-lingual stance detection. Moreover, non-English sources of labelled data are often scarce and present additional challenges. Recently, large multili…
This paper describes our systems for IJCNLP 2017 Shared Task on Customer Feedback Analysis. We experimented with simple neural architectures that gave competitive performance on certain tasks. This includes shallow CNN and Bi-Directional LSTM architectures with Facebook's Fasttext as a baseline model. Our best performing model was in the Top 5 systems using the Exact-Accuracy and Micro-Average-F1 metrics for the Spanish (85.28% for both) and French (70% and 73.17% respectively) task, and outperformed all the other models on comment (87.28%) and meaningless (51.85%) tags using Micro Average F1 …
Exploring Multilingual Concepts of Human Value in Large Language Models: Is Value Alignment Consistent, Transferable and Controllable across Languages?
School of New Media and Communication, Tianjin University（天津大学新媒体与传播学院）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）
We study methods for learning sentence embeddings with syntactic structure. We focus on methods of learning syntactic sentence-embeddings by using a multilingual parallel-corpus augmented by Universal Parts-of-Speech tags. We evaluate the quality of the learned embeddings by examining sentence-level nearest neighbours and functional dissimilarity in the embedding space. We also evaluate the ability of the method to learn syntactic sentence-embeddings for low-resource languages and demonstrate strong evidence for transfer learning. Our results show that syntactic sentence-embeddings can be lear…
XToM: Exploring the Multilingual Theory of Mind for Large Language Models
HKUST, Hong Kong（香港理工大学）；NVIDIA AI Technology Center (NVAITC), USA（NVIDIA AI技术中心（NVAITC））；Shanghai Jiao Tong University, China（上海交通大学）；Technische Universtität Darmstadt, Germany（德累斯顿技术大学）；LMU Munich, Germany（慕尼黑大学）；Munich Center for Machine Learning, Germany（慕尼黑机器学习中心）
Exploring Anisotropy and Outliers in Multilingual Language Models for Cross-Lingual Semantic Sentence Similarity
Center for Information and Language Processing, LMU Munich（慕尼黑大学信息与语言处理中心）；Munich Centre for Machine Learning (MCML)（慕尼黑机器学习中心（MCML））；Faculty of Mathematics and Physics, Charles University（查理大学数学与物理学院）
