% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

@inproceedings{schieb2016governing,
  title={Governing hate speech by means of counterspeech on Facebook},
  author={Schieb, Carla and Preuss, Mike},
  booktitle={66th ica annual conference, at fukuoka, japan},
  pages={1--23},
  year={2016}
}

@article{fanton2021human,
  title={Human-in-the-loop for data collection: a multi-target counter narrative dataset to fight online hate speech},
  author={Fanton, Margherita and Bonaldi, Helena and Tekiroglu, Serra Sinem and Guerini, Marco},
  journal={arXiv preprint arXiv:2107.08720},
  year={2021}
}

@inproceedings{cercas-curry-rieser-2019-crowd,
    title = "A Crowd-based Evaluation of Abuse Response Strategies in Conversational Agents",
    author = "Cercas Curry, Amanda  and
      Rieser, Verena",
    editor = "Nakamura, Satoshi  and
      Gasic, Milica  and
      Zukerman, Ingrid  and
      Skantze, Gabriel  and
      Nakano, Mikio  and
      Papangelis, Alexandros  and
      Ultes, Stefan  and
      Yoshino, Koichiro",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W19-5942/",
    doi = "10.18653/v1/W19-5942",
    pages = "361--366",
    abstract = "How should conversational agents respond to verbal abuse through the user? To answer this question, we conduct a large-scale crowd-sourced evaluation of abuse response strategies employed by current state-of-the-art systems. Our results show that some strategies, such as ``polite refusal'', score highly across the board, while for other strategies demographic factors, such as age, as well as the severity of the preceding abuse influence the user{'}s perception of which response is appropriate. In addition, we find that most data-driven models lag behind rule-based or commercial systems in terms of their perceived appropriateness."
}

@inproceedings{cercas-curry-rieser-2018-metoo,
    title = "{\#}{M}e{T}oo {A}lexa: How Conversational Systems Respond to Sexual Harassment",
    author = "Cercas Curry, Amanda  and
      Rieser, Verena",
    editor = "Alfano, Mark  and
      Hovy, Dirk  and
      Mitchell, Margaret  and
      Strube, Michael",
    booktitle = "Proceedings of the Second {ACL} Workshop on Ethics in Natural Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-0802/",
    doi = "10.18653/v1/W18-0802",
    pages = "7--14",
    abstract = "Conversational AI systems, such as Amazon{'}s Alexa, are rapidly developing from purely transactional systems to social chatbots, which can respond to a wide variety of user requests. In this article, we establish how current state-of-the-art conversational systems react to inappropriate requests, such as bullying and sexual harassment on the part of the user, by collecting and analysing the novel {\#}MeTooAlexa corpus. Our results show that commercial systems mainly avoid answering, while rule-based chatbots show a variety of behaviours and often deflect. Data-driven systems, on the other hand, are often non-coherent, but also run the risk of being interpreted as flirtatious and sometimes react with counter-aggression. This includes our own system, trained on ``clean'' data, which suggests that inappropriate system behaviour is not caused by data bias."
}

@inproceedings{vallecillo-rodriguez-etal-2024-conan,
    title = "{CONAN}-{MT}-{SP}: A {S}panish Corpus for Counternarrative Using {GPT} Models",
    author = "Vallecillo Rodr{\'i}guez, Mar{\'i}a Estrella  and
      Cantero Romero, Maria Victoria  and
      Cabrera De Castro, Isabel  and
      Montejo R{\'a}ez, Arturo  and
      Mart{\'i}n Valdivia, Mar{\'i}a Teresa",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.326/",
    pages = "3677--3688",
    abstract = "This paper describes the automated generation of CounterNarratives (CNs) for Hate Speech (HS) in Spanish using GPT-based models. Our primary objective is to evaluate the performance of these models in comparison to human capabilities. For this purpose, the English CONAN Multitarget corpus is taken as a starting point and we use the DeepL API to automatically translate into Spanish. Two GPT-based models, GPT-3 and GPT-4, are applied to the HS segment through a few-shot prompting strategy to generate a new CN. As a consequence of our research, we have created a high quality corpus in Spanish that includes the original HS-CN pairs translated into Spanish, in addition to the CNs generated automatically with the GPT models and that have been evaluated manually. The resulting CONAN-MT-SP corpus and its evaluation will be made available to the research community, representing the most extensive linguistic resource of CNs in Spanish to date. The results demonstrate that, although the effectiveness of GPT-4 outperforms GPT-3, both models can be used as systems to automatically generate CNs to combat the HS. Moreover, these models consistently outperform human performance in most instances."
}

@inproceedings{liu2024emollms,
  title={Emollms: A series of emotional large language models and annotation tools for comprehensive affective analysis},
  author={Liu, Zhiwei and Yang, Kailai and Xie, Qianqian and Zhang, Tianlin and Ananiadou, Sophia},
  booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages={5487--5496},
  year={2024}
}

@inproceedings{siino-2024-transmistral,
    title = "{T}rans{M}istral at {S}em{E}val-2024 Task 10: Using Mistral 7{B} for Emotion Discovery and Reasoning its Flip in Conversation",
    author = "Siino, Marco",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Tayyar Madabushi, Harish  and
      Da San Martino, Giovanni  and
      Rosenthal, Sara  and
      Ros{\'a}, Aiala},
    booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.semeval-1.46/",
    doi = "10.18653/v1/2024.semeval-1.46",
    pages = "298--304",
    abstract = "The EDiReF shared task at SemEval 2024 comprises three subtasks: Emotion Recognition in Conversation (ERC) in Hindi-English code-mixed conversations, Emotion Flip Reasoning (EFR) in Hindi-English code-mixed conversations, and EFR in English conversations. The objectives for the ERC and EFR tasks are defined as follows: 1) Emotion Recognition in Conversation (ERC): In this task, participants are tasked with assigning an emotion to each utterance within a dialogue from a predefined set of possible emotions. The goal is to accurately recognize and label the emotions expressed in the conversation; 2) Emotion Flip Reasoning (EFR): This task involves identifying the trigger utterance(s) for an emotion-flip within a multi-party conversation dialogue. Participants are required to pinpoint the specific utterance(s) that serve as catalysts for a change in emotion during the conversation. In this paper we only address the first subtask (ERC) making use of an online translation strategy followed by the application of a Mistral 7B model together with a few-shot prompt strategy. Our approach obtains an F1 of 0.36, eventually exhibiting further room for improvements."
}

@inproceedings{10.1145/3603287.3651183,
author = {Stigall, William and Al Hafiz Khan, Md Abdullah and Attota, Dinesh and Nweke, Francis and Pei, Yong},
title = {Large Language Models Performance Comparison of Emotion and Sentiment Classification},
year = {2024},
isbn = {9798400702372},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3603287.3651183},
doi = {10.1145/3603287.3651183},
abstract = {The increasing application of artificial intelligence in daily life necessitates precise emotion classification for improved user interactions in areas like healthcare, marketing, and customer service. This work explores the development of emotion classification algorithms, focusing on text classification and providing low latency inferencing for seamless application into persistent-state systems. We use a parallel multi-task learning approach, to learn multiple tasks with a single loss function, allowing it to learn representations in both Emotion Classification and Sentiment Analysis simultaneously. We present a detailed analysis of a fine-tuned BERTTiny model EmoBERTTiny for emotion and sentiment classification tasks, comparing its performance against baseline models and state-of-the-art 7B parameter models. EmoBERTTiny is bench-marked against Llama-2-7B-chat and Mistral-7B-Instruct across Accuracy, F1-score, precision-recall curves and inference speed. EmoBERTTiny outperforms pre-trained and state-of-the-art models across all metrics and computational efficiency, achieving 93.14\% accuracy in sentiment analysis and 85.48\% accuracy on emotion classification. EmoBERTTiny processes a 256 token context window in 8.04ms on average post-tokenization, and 154.23ms on average in total processing speed.},
booktitle = {Proceedings of the 2024 ACM Southeast Conference},
pages = {60–68},
numpages = {9},
keywords = {BERT, Emotion Classification, Large Language Models, Llama, Mistral, Multitask Models, NLP, Sentiment Analysis},
location = {Marietta, GA, USA},
series = {ACMSE '24}
}

@article{piot2024decoding,
  title={Decoding Hate: Exploring Language Models' Reactions to Hate Speech},
  author={Piot, Paloma and Parapar, Javier},
  journal={arXiv preprint arXiv:2410.00775},
  year={2024}
}

@misc{xiang2023openai,
  title={OpenAI used Kenyan workers making \$2 an hour to filter traumatic content from ChatGPT},
  author={Xiang, Chloe},
  year={2023},
  publisher={VICE}
}
@article{flesch2007flesch,
  title={Flesch-Kincaid readability test},
  author={Flesch, Rudolf},
  journal={Retrieved October},
  volume={26},
  number={3},
  pages={2007},
  year={2007}
}
@article{chung2024scaling,
  title={Scaling instruction-finetuned language models},
  author={Chung, Hyung Won and Hou, Le and Longpre, Shayne and Zoph, Barret and Tay, Yi and Fedus, William and Li, Yunxuan and Wang, Xuezhi and Dehghani, Mostafa and Brahma, Siddhartha and others},
  journal={Journal of Machine Learning Research},
  volume={25},
  number={70},
  pages={1--53},
  year={2024}
}

@incollection{mathew2020interaction,
  title={Interaction dynamics between hate and counter users on Twitter},
  author={Mathew, Binny and Kumar, Navish and Goyal, Pawan and Mukherjee, Animesh},
  booktitle={Proceedings of the 7th ACM IKDD CoDS and 25th COMAD},
  pages={116--124},
  year={2020}
}
@inproceedings{bonaldi2025first,
  title={The First Workshop on Multilingual Counterspeech Generation at COLING 2025: Overview of the Shared Task},
  author={Bonaldi, Helena and Vallecillo-Rodr{\'\i}guez, Mar{\'\i}a Estrella and Zubiaga, Irune and Montejo-R{\'a}ez, Arturo and Soroa, Aitor and Mart{\'\i}n-Valdivia, Mar{\'\i}a-Teresa and Guerini, Marco and Agerri, Rodrigo},
  booktitle={Proceedings of the First Workshop on Multilingual Counterspeech Generation},
  pages={92--107},
  year={2025}
}
@article{bonaldi2022human,
  title={Human-machine collaboration approaches to build a dialogue dataset for hate speech countering},
  author={Bonaldi, Helena and Dellantonio, Sara and Tekiroglu, Serra Sinem and Guerini, Marco},
  journal={arXiv preprint arXiv:2211.03433},
  year={2022}
}
@article{bonaldi2024nlp,
  title={{NLP} for Counterspeech against Hate: A Survey and How-To Guide},
  author={Bonaldi, Helena and Chung, Yi-Ling and Abercrombie, Gavin and Guerini, Marco},
  booktitle={Findings of the Association for Computational Linguistics: NAACL 2024},
  pages={3480--3499},
  year={2024}
}

@inproceedings{cima2025contextualized,
  title={Contextualized counterspeech: Strategies for adaptation, personalization, and evaluation},
  author={Cima, Lorenzo and Miaschi, Alessio and Trujillo, Amaury and Avvenuti, Marco and Dell'Orletta, Felice and Cresci, Stefano},
  booktitle={Proceedings of the ACM on Web Conference 2025},
  pages={5022--5033},
  year={2025}
}
@article{ghenai2025exploring,
  title={Exploring hate speech dynamics: The emotional, linguistic, and thematic impact on social media users},
  author={Ghenai, Amira and Noorian, Zeinab and Moradisani, Hadiseh and Abadeh, Parya and Erentzen, Caroline and Zarrinkalam, Fattane},
  journal={Information Processing \& Management},
  volume={62},
  number={3},
  pages={104079},
  year={2025},
  publisher={Elsevier}
}
@article{radford2019language,
  title={Language models are unsupervised multitask learners},
  author={Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya and others},
  journal={OpenAI blog},
  volume={1},
  number={8},
  pages={9},
  year={2019}
}
@inproceedings{zhang-etal-2020-dialogpt,
    title = "{DIALOGPT} : Large-Scale Generative Pre-training for Conversational Response Generation",
    author = "Zhang, Yizhe  and
      Sun, Siqi  and
      Galley, Michel  and
      Chen, Yen-Chun  and
      Brockett, Chris  and
      Gao, Xiang  and
      Gao, Jianfeng  and
      Liu, Jingjing  and
      Dolan, Bill",
    editor = "Celikyilmaz, Asli  and
      Wen, Tsung-Hsien",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-demos.30/",
    doi = "10.18653/v1/2020.acl-demos.30",
    pages = "270--278",
    abstract = "We present a large, tunable neural conversational response generation model, DIALOGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human both in terms of automatic and human evaluation in single-turn dialogue settings. We show that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems. The pre-trained model and training pipeline are publicly released to facilitate research into neural response generation and the development of more intelligent open-domain dialogue systems."
}
@article{saha2024zero,
  title={On zero-shot counterspeech generation by LLMs},
  author={Saha, Punyajoy and Agrawal, Aalok and Jana, Abhik and Biemann, Chris and Mukherjee, Animesh},
  journal={arXiv preprint arXiv:2403.14938},
  year={2024}
}




@inproceedings{plaza2021multi,
  title={Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language},
  author={Plaza-del-Arco, Flor Miriam and Halat, Sercan and Pad{\'o}, Sebastian and Klinger, Roman},
  booktitle={Working Notes of FIRE 2021: Forum for Information Retrieval Evaluation Gandhinagar, India, December 13-17, 2021.},
  year={2021}
}
@inproceedings{ashida2022towards,
  title={Towards automatic generation of messages countering online hate speech and microaggressions},
  author={Ashida, Mana and Komachi, Mamoru},
  booktitle={Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)},
  pages={11--23},
  year={2022}
}

@inproceedings{chung2020italian,
  title={Italian Counter Narrative Generation to Fight Online Hate Speech.},
  author={Chung, Yi-Ling and Tekiroglu, Serra Sinem and Guerini, Marco and others},
  booktitle={CLiC-it},
  year={2020}
}

@article{ngueajio2025decoding,
  title={Decoding Fake News and Hate Speech: A Survey of Explainable AI Techniques},
  author={Ngueajio, Mikel K and Aryal, Saurav and Atemkeng, Marcellin and Washington, Gloria and Rawat, Danda},
  journal={ACM Computing Surveys},
  volume={57},
  number={7},
  pages={1--37},
  year={2025},
  publisher={ACM New York, NY}
}

@article{chung2023understanding,
  title={Understanding counterspeech for online harm mitigation},
  author={Chung, Yi-Ling and Abercrombie, Gavin and Enock, Florence and Bright, Jonathan and Rieser, Verena},
  journal={arXiv preprint arXiv:2307.04761},
  year={2023}
}

@article{godel2021moderating,
  title={Moderating with the mob: Evaluating the efficacy of real-time crowdsourced fact-checking},
  author={Godel, William and Sanderson, Zeve and Aslett, Kevin and Nagler, Jonathan and Bonneau, Richard and Persily, Nathaniel and Tucker, Joshua A},
  journal={Journal of Online Trust and Safety},
  volume={1},
  number={1},
  year={2021}
}

@inproceedings{bonaldi-etal-2024-nlp,
  title={NLP for Counterspeech against Hate: A Survey and How-To Guide},
  author={Bonaldi, Helena and Chung, Yi-Ling and Abercrombie, Gavin and Guerini, Marco},
  booktitle={Findings of the Association for Computational Linguistics: NAACL 2024},
  pages={3480--3499},
  year={2024}
}



@article{chung2021empowering,
  title={Empowering NGOs in countering online hate messages. Online Social Networks and Media 24 (2021), 100150},
  author={Chung, Yi-Ling and Tekiro{\u{g}}lu, Serra Sinem and Tonelli, Sara and Guerini, Marco},
  journal={URL: https://www. sciencedirect. com/science/article/pii/S246869642100032X. doi: https://doi. org/10.1016/j. osnem},
  year={2021}
}

@inproceedings{piot2024metahate,
  title={Metahate: A dataset for unifying efforts on hate speech detection},
  author={Piot, Paloma and Mart{\'\i}n-Rodilla, Patricia and Parapar, Javier},
  booktitle={Proceedings of the International AAAI Conference on Web and Social Media},
  volume={18},
  pages={2025--2039},
  year={2024}
}

@inproceedings{demszky-etal-2020-goemotions,
    title = "{G}o{E}motions: A Dataset of Fine-Grained Emotions",
    author = "Demszky, Dorottya  and
      Movshovitz-Attias, Dana  and
      Ko, Jeongwoo  and
      Cowen, Alan  and
      Nemade, Gaurav  and
      Ravi, Sujith",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.372/",
    doi = "10.18653/v1/2020.acl-main.372",
    pages = "4040--4054",
    abstract = "Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to detecting harmful online behavior. Advancement in this area can be improved using large-scale datasets with a fine-grained typology, adaptable to multiple downstream tasks. We introduce GoEmotions, the largest manually annotated dataset of 58k English Reddit comments, labeled for 27 emotion categories or Neutral. We demonstrate the high quality of the annotations via Principal Preserved Component Analysis. We conduct transfer learning experiments with existing emotion benchmarks to show that our dataset generalizes well to other domains and different emotion taxonomies. Our BERT-based model achieves an average F1-score of .46 across our proposed taxonomy, leaving much room for improvement."
}

@article{grattafiori2024llama,
  title={The llama 3 herd of models},
  author={Grattafiori, Aaron and Dubey, Abhimanyu and Jauhri, Abhinav and Pandey, Abhinav and Kadian, Abhishek and Al-Dahle, Ahmad and Letman, Aiesha and Mathur, Akhil and Schelten, Alan and Vaughan, Alex and others},
  journal={arXiv preprint arXiv:2407.21783},
  year={2024}
}

@article{hurst2024gpt,
  title={Gpt-4o system card},
  author={Hurst, Aaron and Lerer, Adam and Goucher, Adam P and Perelman, Adam and Ramesh, Aditya and Clark, Aidan and Ostrow, AJ and Welihinda, Akila and Hayes, Alan and Radford, Alec and others},
  journal={arXiv preprint arXiv:2410.21276},
  year={2024}
}

@article{jiang2023mistral,
  title={Mistral 7b. arxiv},
  author={Jiang, Albert Q and Sablayrolles, A and Mensch, A and Bamford, C and Chaplot, D Singh and Casas, Ddl and Bressand, F and Lengyel, G and Lample, G and Saulnier, L and others},
  journal={arXiv preprint arXiv:2310.06825},
  volume={10},
  year={2023}
}

@inproceedings{nevsic2024advancing,
  title={Advancing Sentiment Analysis in Serbian Literature: A Zero and Few--Shot Learning Approach Using the Mistral Model},
  author={Ne{\v{s}}i{\'c}, Milica Ikoni{\'c} and Petalinkar, Sa{\v{s}}a and {\v{S}}kori{\'c}, Mihailo and Stankovi{\'c}, Ranka and Rujevi{\'c}, Biljana},
  booktitle={Proceedings of the Sixth International Conference on Computational Linguistics in Bulgaria (CLIB 2024)},
  pages={58--70},
  year={2024}
}

@article{gupta2023bias,
  title={Bias runs deep: Implicit reasoning biases in persona-assigned llms},
  author={Gupta, Shashank and Shrivastava, Vaishnavi and Deshpande, Ameet and Kalyan, Ashwin and Clark, Peter and Sabharwal, Ashish and Khot, Tushar},
  journal={arXiv preprint arXiv:2311.04892},
  year={2023}
}
@inproceedings{basile2019semeval,
  title={Semeval-2019 task 5: Multilingual detection of hate speech against immigrants and women in twitter},
  author={Basile, Valerio and Bosco, Cristina and Fersini, Elisabetta and Nozza, Debora and Patti, Viviana and Pardo, Francisco Manuel Rangel and Rosso, Paolo and Sanguinetti, Manuela},
  booktitle={Proceedings of the 13th international workshop on semantic evaluation},
  pages={54--63},
  year={2019}
}
@inproceedings{ngueajio2022hey,
  title={Hey ASR system! Why aren’t you more inclusive? Automatic speech recognition systems’ bias and proposed bias mitigation techniques. A literature review},
  author={Ngueajio, Mikel K and Washington, Gloria},
  booktitle={International conference on human-computer interaction},
  pages={421--440},
  year={2022},
  organization={Springer}
}
@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
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@inproceedings{andrew2007scalable,
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@book{Gusfield:97,
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@article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {Yara Parser: {A} Fast and Accurate Dependency Parser},
    journal   = {Computing Research Repository},
    volume    = {arXiv:1503.06733},
    year      = {2015},
    url       = {http://arxiv.org/abs/1503.06733},
    note    = {version 2}
}

@article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},
	Pages = {1817--1853},
	Publisher = {JMLR.org},
	Title = {A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},
	Volume = {6},
	Year = {2005}
}

@inproceedings{plaza-etal-2024-countering,
    title = "Countering Hateful and Offensive Speech Online - Open Challenges",
    author = "Plaza-del-Arco, Flor Miriam  and
      Nozza, Debora  and
      Guerini, Marco  and
      Sorensen, Jeffrey  and
      Zampieri, Marcos",
    editor = "Li, Jessy  and
      Liu, Fei",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-tutorials.2/",
    doi = "10.18653/v1/2024.emnlp-tutorials.2",
    pages = "11--16"
}

@inproceedings{tekiroglu-etal-2020-generating,
    title = "Generating Counter Narratives against Online Hate Speech: Data and Strategies",
    author = "Tekiro{\u{g}}lu, Serra Sinem  and
      Chung, Yi-Ling  and
      Guerini, Marco",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.110/",
    doi = "10.18653/v1/2020.acl-main.110",
    pages = "1177--1190",
    abstract = "Recently research has started focusing on avoiding undesired effects that come with content moderation, such as censorship and overblocking, when dealing with hatred online. The core idea is to directly intervene in the discussion with textual responses that are meant to counter the hate content and prevent it from further spreading. Accordingly, automation strategies, such as natural language generation, are beginning to be investigated. Still, they suffer from the lack of sufficient amount of quality data and tend to produce generic/repetitive responses. Being aware of the aforementioned limitations, we present a study on how to collect responses to hate effectively, employing large scale unsupervised language models such as GPT-2 for the generation of silver data, and the best annotation strategies/neural architectures that can be used for data filtering before expert validation/post-editing."
}

@inproceedings{bonaldi-etal-2023-weigh,
    title = "Weigh Your Own Words: Improving Hate Speech Counter Narrative Generation via Attention Regularization",
    author = "Bonaldi, Helena  and
      Attanasio, Giuseppe  and
      Nozza, Debora  and
      Guerini, Marco",
    editor = "Chung, Yi-Ling  and
      Bonaldi, Helena  and
      Abercrombie, Gavin  and
      Guerini, Marco",
    booktitle = "Proceedings of the 1st Workshop on CounterSpeech for Online Abuse (CS4OA)",
    month = sep,
    year = "2023",
    address = "Prague, Czechia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.cs4oa-1.2/",
    pages = "13--28",
    abstract = "Recent computational approaches for combating online hate speech involve the automatic generation of counter narratives by adapting Pretrained Transformer-based Language Models (PLMs) with human-curated data. This process, however, can produce in-domain overfitting, resulting in models generating acceptable narratives only for hatred similar to training data, with little portability to other targets or to real-world toxic language. This paper introduces novel attention regularization methodologies to improve the generalization capabilities of PLMs for counter narratives generation. Overfitting to training-specific terms is then discouraged, resulting in more diverse and richer narratives. We experiment with two attention-based regularization techniques on a benchmark English dataset. Regularized models produce better counter narratives than state-of-the-art approaches in most cases, both in terms of automatic metrics and human evaluation, especially when hateful targets are not present in the training data. This work paves the way for better and more flexible counter-speech generation models, a task for which datasets are highly challenging to produce."
}

@article{plaza2022integrating,
  title={Integrating implicit and explicit linguistic phenomena via multi-task learning for offensive language detection},
  author={{Plaza-del-Arco}, Flor Miriam and Molina-Gonz{\'a}lez, M Dolores and Ure{\~n}a-L{\'o}pez, L Alfonso and Mart{\'\i}n-Valdivia, Mar{\'\i}a-Teresa},
  journal={Knowledge-Based Systems},
  volume={258},
  pages={109965},
  year={2022},
  publisher={Elsevier}
}

@inproceedings{moscato-etal-2025-mnlp,
    title = "{MilaNLP}@Multilingual Counterspeech Generation: Evaluating Translation and Background Knowledge Filtering",
    author = "Moscato, Emanuele  and
      Muti, Arianna  and
      Nozza, Debora",
    editor = "Bonaldi, Helena  and
      Vallecillo-Rodr{\'i}guez, Mar{\'i}a Estrella  and
      Zubiaga, Irune  and
      Montejo-R{\'a}ez, Arturo  and
      Soroa, Aitor  and
      Mart{\'i}n-Valdivia, Mar{\'i}a Teresa  and
      Guerini, Marco  and
      Agerri, Rodrigo",
    booktitle = "Proceedings of the First Workshop on Multilingual Counterspeech Generation",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.mcg-1.7/",
    pages = "56--64",
    abstract = "We describe our participation in the Multilingual Counterspeech Generation shared task, which aims to generate a counternarrative to counteract hate speech, given a hateful sentence and relevant background knowledge. Our team tested two different aspects: translating outputs from English vs generating outputs in the original languages and filtering pieces of the background knowledge provided vs including all the background knowledge. Our experiments show that filtering the background knowledge in the same prompt and leaving data in the original languages leads to more adherent counternarrative generations, except for Basque, where translating the output from English and filtering the background knowledge in a separate prompt yields better results. Our system ranked first in English, Italian, and Spanish and fourth in Basque."
}
