The growing interest in employing counter narratives for hatred intervention brings with it a focus on dataset creation and automation strategies. In this scenario, learning to recognize counter narrative types from natural text is expected to be useful for applications such as hate speech countering, where operators from non-governmental organizations are supposed to answer to hate with several and diverse arguments that can be mined from online sources. This paper presents the first multilingual work on counter narrative type classification, evaluating SoTA pre-trained language models in monolingual, multilingual and cross-lingual settings. When considering a fine-grained annotation of counter narrative classes, we report strong baseline classification results for the majority of the counter narrative types, especially if we translate every language to English before cross-lingual prediction. This suggests that knowledge about counter narratives can be successfully transferred across languages.
BIASEDTALES-ML: A Multilingual Dataset for Analyzing Narrative Attribute Distributions in LLM-Generated Stories
School of Computer Science and Engineering, Northeastern University, Shenyang, China（东北大学计算机科学与工程学院，中国沈阳）；NiuTrans Research, Shenyang, China（NiuTrans研究院，中国沈阳）
PartisanLens: A Multilingual Dataset of Hyperpartisan and Conspiratorial Immigration Narratives in European Media
Centro Singular de Investigación en Tecnoloxías Intelixentes da USC（乌拉特大学智能技术研究中心）；IRLab, CITIC Research Centre, Universidade da Coruña（IR实验室，CITIC研究中心，科鲁纳大学）；Universidade de Évora（埃夫拉大学）；Universidad de La Rioja（里瓦达维亚大学）；GESIS Leibniz Institute for the Social Sciences（莱比锡社会科学院）
CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；College of Software, Jilin University（吉林大学软件学院）；Xingchen AGI Lab, China Telecom Artificial Intelligence Technology (Beijing) Co., Ltd.（中国电信人工智能技术（北京）有限公司星辰AGI实验室）；Bilibili Inc.（哔哩哔哩公司）；University of Ottawa（渥太华大学）；International Center of Future Science, Jilin University（吉林大学未来科学国际中心）
Reliable Multilingual Orthopedic Decision Support from Clinical Narratives: Language-Aware Adaptation and Verification-Guided Deferral
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Software, Nanjing University of Information Science and Technology (NUIST)（南京信息工程大学软件学院）；Department of Orthopedic, Bahawal Victoria Hospital（巴哈瓦尔维多利亚医院骨科部门）
Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）；University of Trento（特伦托大学）；University of Stuttgart（斯图加特大学）
In this work, we present an extensive study on the use of pre-trained language models for the task of automatic Counter Narrative (CN) generation to fight online hate speech in English. We first present a comparative study to determine whether there is a particular Language Model (or class of LMs) and a particular decoding mechanism that are the most appropriate to generate CNs. Findings show that autoregressive models combined with stochastic decodings are the most promising. We then investigate how an LM performs in generating a CN with regard to an unseen target of hate. We find out that a …
Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation
Technische Universität Berlin（柏林工业大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；University of Marburg（马尔堡大学）；LMU Munich（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心（MCML））；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所（BIFOLD））
Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）；University of Trento（特伦托大学）
Although there is an unprecedented effort to provide adequate responses in terms of laws and policies to hate content on social media platforms, dealing with hatred online is still a tough problem. Tackling hate speech in the standard way of content deletion or user suspension may be charged with censorship and overblocking. One alternate strategy, that has received little attention so far by the research community, is to actually oppose hate content with counter-narratives (i.e. informed textual responses). In this paper, we describe the creation of the first large-scale, multilingual, expert…
Deconstructing Stereotypes: Scope-Conditioned Generation for Effective Multilingual Counterspeech
Université Côte d’Azur（蔚蓝海岸大学）；CNRS（法国国家科学研究中心）；INRIA（法国国家信息与自动化研究所）；I3S（I3S实验室）；PRHLT Research Center, Universitat Politècnica de València（瓦伦西亚理工大学PRHLT研究中心）；ValgrAI Valencian Graduate School and Research Network of Artificial Intelligence（ValgrAI瓦伦西亚人工智能研究生院与研究网络）
LLMs for automatic annotation of Mandarin narrative transcripts
Shanghai Jiao Tong University（上海交通大学）；University of California San Diego（加州大学圣地亚哥分校）；The Hong Kong Polytechnic University（香港理工大学）；National Research Center for Language and Well-Being（语言与幸福感国家研究中心）；City University of Hong Kong（香港城市大学）
The Chinese University of Hong Kong（香港中文大学）；Alibaba Group（阿里巴巴集团）；Peking University（北京大学）
Structured pruning has been extensively studied on monolingual pre-trained language models and is yet to be fully evaluated on their multilingual counterparts. This work investigates three aspects of structured pruning on multilingual pre-trained language models: settings, algorithms, and efficiency. Experiments on nine downstream tasks show several counter-intuitive phenomena: for settings, individually pruning for each language does not induce a better result; for algorithms, the simplest method performs the best; for efficiency, a fast model does not imply that it is also small. To facilita…
Research Centre for Cognitive Science & Artificial Intelligence（认知科学与人工智能研究中心）；Department of Computational Cognitive Science（计算认知科学系）；Tilburg University（蒂尔堡大学）
MuRating: A High Quality Data Selecting Approach to Multilingual Large Language Model Pretraining
Hong Kong University of Science and Technology (Guangzhou)（香港理工大学（广州））；ByteDance（字节跳动）；Eindhoven University of Technology（埃因霍温理工大学）；University of Melbourne（墨尔本大学）；University of Liverpool（利物浦大学）
Northeastern Uni at Multilingual Counterspeech Generation: Enhancing Counter Speech Generation with LLM Alignment through Direct Preference Optimization
Capital One（第一资本）；Northeastern University（东北大学）；Apple Inc.（苹果公司）
Macro: Enhancing Multilingual Counterfactual Explanations through Alignment-as-Preference Optimization
Technische Universität Berlin（柏林技术大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；University of Duisburg-Essen（杜伊斯堡- Essen大学）；LMU Munich（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Saarland Informatics Campus（萨尔兰州信息学校区）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究院）；Centre for European Research in Trusted AI (CERTAIN)（可信人工智能欧洲研究中心）
Using Instruction-Tuned Large Language Models to Identify Indicators of Vulnerability in Police Incident Narratives
School for Business and Society, University of York（约克大学商业与社会学院）；ESRC Vulnerability and Policing Futures Research Centre（经济与社会研究委员会脆弱性与警务未来研究中心）；School of Law, University of Leeds（利兹大学法学院）
Multilingual Synopses of Movie Narratives: A Dataset for Vision-Language Story Understanding
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）
Fondazione Bruno Kessler（布鲁诺·凯塞勒基金会）；University of Trento（特伦托大学）
Tackling online hatred using informed textual responses - called counter narratives - has been brought under the spotlight recently. Accordingly, a research line has emerged to automatically generate counter narratives in order to facilitate the direct intervention in the hate discussion and to prevent hate content from further spreading. Still, current neural approaches tend to produce generic/repetitive responses and lack grounded and up-to-date evidence such as facts, statistics, or examples. Moreover, these models can create plausible but not necessarily true arguments. In this paper we pr…
Specific lexical choices in narrative text reflect both the writer's attitudes towards people in the narrative and influence the audience's reactions. Prior work has examined descriptions of people in English using contextual affective analysis, a natural language processing (NLP) technique that seeks to analyze how people are portrayed along dimensions of power, agency, and sentiment. Our work presents an extension of this methodology to multilingual settings, which is enabled by a new corpus that we collect and a new multilingual model. We additionally show how word connotations differ acros…
MCEval: A Dynamic Framework for Fair Multilingual Cultural Evaluation of LLMs
Zhejiang University（浙江大学）；School of Engineering, Westlake University（西溪大学工程学院）；Southern University of Science and Technology（南方科技大学）；Institute of Advanced Technology, Westlake Institute for Advanced Study（西溪研究院先进技术研究所）
University of Trento（特伦托大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
Undermining the impact of hateful content with informed and non-aggressive responses, called counter narratives, has emerged as a possible solution for having healthier online communities. Thus, some NLP studies have started addressing the task of counter narrative generation. Although such studies have made an effort to build hate speech / counter narrative (HS/CN) datasets for neural generation, they fall short in reaching either high-quality and/or high-quantity. In this paper, we propose a novel human-in-the-loop data collection methodology in which a generative language model is refined i…
Agenda-based Narrative Extraction: Steering Pathfinding Algorithms with Large Language Models
Department of Computing and Systems Engineering, Universidad Católica del Norte, Antofagasta, Chile（智利天主教大学北部校区计算与系统工程系，安托法加斯塔，智利）；Department of Industrial Engineering, Universidad Católica del Norte, Antofagasta, Chile（智利天主教大学北部校区工业工程系，安托法加斯塔，智利）；Department of Chemical and Environmental Engineering, Universidad Católica del Norte, Antofagasta, Chile（智利天主教大学北部校区化学与环境工程系，安托法加斯塔，智利）
