@inproceedings{teruel-etal-2018-increasing,
    title = "Increasing Argument Annotation Reproducibility by Using Inter-annotator Agreement to Improve Guidelines",
    author = "Teruel, Milagro  and
      Cardellino, Cristian  and
      Cardellino, Fernando  and
      Alonso Alemany, Laura  and
      Villata, Serena",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://aclanthology.org/L18-1640",
}

@inproceedings{stab-gurevych-2014-identifying,
    title = "Identifying Argumentative Discourse Structures in Persuasive Essays",
    author = "Stab, Christian  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing ({EMNLP})",
    month = oct,
    year = "2014",
    address = "Doha, Qatar",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D14-1006",
    doi = "10.3115/v1/D14-1006",
    pages = "46--56",
}

@article{habernal-gurevych-2017-argumentation,
    title = "Argumentation Mining in User-Generated Web Discourse",
    author = "Habernal, Ivan  and
      Gurevych, Iryna",
    journal = "Computational Linguistics",
    volume = "43",
    number = "1",
    month = apr,
    year = "2017",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://aclanthology.org/J17-1004",
    doi = "10.1162/COLI_a_00276",
    pages = "125--179",
    abstract = "The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people{'}s argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.",
}

@inproceedings{al-khatib-etal-2016-news,
    title = "A News Editorial Corpus for Mining Argumentation Strategies",
    author = "Al-Khatib, Khalid  and
      Wachsmuth, Henning  and
      Kiesel, Johannes  and
      Hagen, Matthias  and
      Stein, Benno",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://aclanthology.org/C16-1324",
    pages = "3433--3443",
    abstract = "Many argumentative texts, and news editorials in particular, follow a specific strategy to persuade their readers of some opinion or attitude. This includes decisions such as when to tell an anecdote or where to support an assumption with statistics, which is reflected by the composition of different types of argumentative discourse units in a text. While several argument mining corpora have recently been published, they do not allow the study of argumentation strategies due to incomplete or coarse-grained unit annotations. This paper presents a novel corpus with 300 editorials from three diverse news portals that provides the basis for mining argumentation strategies. Each unit in all editorials has been assigned one of six types by three annotators with a high Fleiss{'} Kappa agreement of 0.56. We investigate various challenges of the annotation process and we conduct a first corpus analysis. Our results reveal different strategies across the news portals, exemplifying the benefit of studying editorials{---}a so far underresourced text genre in argument mining.",
}

@inproceedings{teufel-etal-1999-annotation,
    title = "An annotation scheme for discourse-level argumentation in research articles",
    author = "Teufel, Simone  and
      Carletta, Jean  and
      Moens, Marc",
    booktitle = "Ninth Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = jun,
    year = "1999",
    address = "Bergen, Norway",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/E99-1015",
    pages = "110--117",
}

@inproceedings{schaefer-stede-2020-annotation,
    title = "Annotation and Detection of Arguments in Tweets",
    author = "Schaefer, Robin  and
      Stede, Manfred",
    booktitle = "Proceedings of the 7th Workshop on Argument Mining",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.argmining-1.6",
    pages = "53--58",
    abstract = "Notwithstanding the increasing role Twitter plays in modern political and social discourse, resources built for conducting argument mining on tweets remain limited. In this paper, we present a new corpus of German tweets annotated for argument components. To the best of our knowledge, this is the first corpus containing not only annotated full tweets but also argumentative spans within tweets. We further report first promising results using supervised classification (F1: 0.82) and sequence labeling (F1: 0.72) approaches.",
}

@inproceedings{bhatti-etal-2021-argument,
    title = "Argument Mining on {T}witter: A Case Study on the Planned Parenthood Debate",
    author = "Bhatti, Muhammad Mahad Afzal  and
      Ahmad, Ahsan Suheer  and
      Park, Joonsuk",
    booktitle = "Proceedings of the 8th Workshop on Argument Mining",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.argmining-1.1",
    doi = "10.18653/v1/2021.argmining-1.1",
    pages = "1--11",
    abstract = "Twitter is a popular platform to share opinions and claims, which may be accompanied by the underlying rationale. Such information can be invaluable to policy makers, marketers and social scientists, to name a few. However, the effort to mine arguments on Twitter has been limited, mainly because a tweet is typically too short to contain an argument {---} both a claim and a premise. In this paper, we propose a novel problem formulation to mine arguments from Twitter: We formulate argument mining on Twitter as a text classification task to identify tweets that serve as premises for a hashtag that represents a claim of interest. To demonstrate the efficacy of this formulation, we mine arguments for and against funding Planned Parenthood expressed in tweets. We first present a new dataset of 24,100 tweets containing hashtag {\#}StandWithPP or {\#}DefundPP, manually labeled as SUPPORT WITH REASON, SUPPORT WITHOUT REASON, and NO EXPLICIT SUPPORT. We then train classifiers to determine the types of tweets, achieving the best performance of 71{\%} F1. Our results manifest claim-specific keywords as the most informative features, which in turn reveal prominent arguments for and against funding Planned Parenthood.",
}

@inbook{vanEemeren,
author = {van Eemeren, F. H., & Grootendorst, R.},
year = {1992},
publisher = {Lawrence Erlbaum Associates, Inc.},
title = {Argumentation, communication, and fallacies: A pragma-dialectical perspective.}
}

@inbook{sellars,
author = {Sellars, Andrew},
year = {2016},
month = {12},
pages = {10-26},
publisher = {Berkman Klein Center Research Publication No. 2016-20, Boston Univ. School of Law, Public Law Research Paper No. 16-48},
title = {Defining Hate Speech},
ssrn = {https://ssrn.com/abstract=2882244},
doi = {http://dx.doi.org/10.2139/ssrn.2882244}
}

@dataset{schaefer_stede_2022,
  author       = {Robin Schaefer and
                  Manfred Stede},
  title        = {German Climate Change Tweet Corpus (GerCCT)},
  month        = apr,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {v1.0},
  doi          = {10.5281/zenodo.6479492},
  url          = {https://doi.org/10.5281/zenodo.6479492}
}

@article{Schaefer2021ArgumentMO,
  title={Argument Mining on Twitter: A survey},
  author={Robin Schaefer and Manfred Stede},
  journal={it - Information Technology},
  year={2021},
  volume={63},
  pages={45 - 58}
}

@article{citron2011intermediaries,
  title={Intermediaries and hate speech: Fostering digital citizenship for our information age},
  author={Citron, Danielle Keats and Norton, Helen},
  journal={BUL Rev.},
  volume={91},
  pages={1435},
  year={2011},
  publisher={HeinOnline}
}

@article{chung2019conan,
   title={CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech},
   url={http://dx.doi.org/10.18653/v1/P19-1271},
   DOI={10.18653/v1/p19-1271},
   journal={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
   publisher={Association for Computational Linguistics},
   author={Chung, Yi-Ling and Kuzmenko, Elizaveta and Tekiroglu, Serra Sinem and Guerini, Marco},
   year={2019}
}

@article{LawrenceEtAl,
    title={A decision tree for annotating argumentation scheme corpora},
    journal={3rd European Conference on Argumentation, Groningen, Netherlands},
    author={Lawrence, John and Visser, Jacky and Walton, Douglas and Reed, Chris},
    year={2020}
}

@article{wagemansPTA,
    title={Constructing a Periodic Table of Arguments},
    journal={Proceedings of the 11th International Conference of the Ontario Society for the Study of Argumentation (OSSA), Windsor, ON: OSSA},
    author={Wagemans, Jean H.M.},
    year={2016}
}

@misc{xlm-roberta,
  doi = {10.48550/ARXIV.1911.02116},
  
  url = {https://arxiv.org/abs/1911.02116},
  
  author = {Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzmán, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin},
  
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Unsupervised Cross-lingual Representation Learning at Scale},
  
  publisher = {arXiv},
  
  year = {2019},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

%Lawrence, J., Visser, J., Walton, D., & Reed, C. (2020). A decision tree for annotating argumentation scheme corpora. 97-114. Paper presented at 3rd European Conference on Argumentation, Groningen, Netherlands

@article{DBLP:journals/corr/abs-1909-04251,
  author    = {Jing Qian and
               Anna Bethke and
               Yinyin Liu and
               Elizabeth M. Belding and
               William Yang Wang},
  title     = {A Benchmark Dataset for Learning to Intervene in Online Hate Speech},
  journal   = {CoRR},
  volume    = {abs/1909.04251},
  year      = {2019},
  url       = {http://arxiv.org/abs/1909.04251},
  eprinttype = {arXiv},
  eprint    = {1909.04251},
  timestamp = {Tue, 17 Sep 2019 11:23:44 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1909-04251.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{annotatingArgumentSchemesVisser,
author = {Visser, Jacky and Lawrence, John and Reed, Chris and Wagemans, Jean and Walton, Douglas},
year = {2021},
month = {03},
pages = {},
title = {Annotating Argument Schemes},
volume = {35},
journal = {Argumentation},
doi = {10.1007/s10503-020-09519-x}
}

@inbook{macagnoArgSchemes2018,
author = {Macagno, Fabrizio and Walton, Douglas and Reed, Chris},
year = {2018},
month = {08},
pages = {517-574},
title = {Argumentation Schemes}
}

@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}


@article{bojanowski2016enriching,
  title={Enriching Word Vectors with Subword Information},
  author={Bojanowski, Piotr and Grave, Edouard and Joulin, Armand and Mikolov, Tomas},
  journal={arXiv preprint arXiv:1607.04606},
  year={2016}
}

@inproceedings{dusmanu-etal-2017-argument,
    title = "Argument Mining on {T}witter: Arguments, Facts and Sources",
    author = "Dusmanu, Mihai  and
      Cabrio, Elena  and
      Villata, Serena",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D17-1245",
    doi = "10.18653/v1/D17-1245",
    pages = "2317--2322",
    abstract = "Social media collect and spread on the Web personal opinions, facts, fake news and all kind of information users may be interested in. Applying argument mining methods to such heterogeneous data sources is a challenging open research issue, in particular considering the peculiarities of the language used to write textual messages on social media. In addition, new issues emerge when dealing with arguments posted on such platforms, such as the need to make a distinction between personal opinions and actual facts, and to detect the source disseminating information about such facts to allow for provenance verification. In this paper, we apply supervised classification to identify arguments on Twitter, and we present two new tasks for argument mining, namely facts recognition and source identification. We study the feasibility of the approaches proposed to address these tasks on a set of tweets related to the Grexit and Brexit news topics.",
}

@misc{elmo,
  doi = {10.48550/ARXIV.1802.05365},
  
  url = {https://arxiv.org/abs/1802.05365},
  
  author = {Peters, Matthew E. and Neumann, Mark and Iyyer, Mohit and Gardner, Matt and Clark, Christopher and Lee, Kenton and Zettlemoyer, Luke},
  
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Deep contextualized word representations},
  
  publisher = {arXiv},
  
  year = {2018},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@inproceedings{komninos-2016,
    title = "Dependency Based Embeddings for Sentence Classification Tasks",
    author = "Komninos, Alexandros  and
      Manandhar, Suresh",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N16-1175",
    doi = "10.18653/v1/N16-1175",
    pages = "1490--1500",
}

@misc{reimers,
author = "Reimers Nils and Gurevych Iryna",
title = "Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
year = "2017",
}


@book{toulmin_2003, place={Cambridge}, edition={2}, title={The Uses of Argument}, DOI={10.1017/CBO9780511840005}, publisher={Cambridge University Press}, author={Toulmin, Stephen E.}, year={2003}}

@inproceedings{polignano2019alberto,
  title={Alberto: Italian BERT language understanding model for NLP challenging tasks based on tweets},
  author={Polignano, Marco and Basile, Pierpaolo and De Gemmis, Marco and Semeraro, Giovanni and Basile, Valerio},
  booktitle={6th Italian Conference on Computational Linguistics, CLiC-it 2019},
  volume={2481},
  pages={1--6},
  year={2019},
  organization={CEUR}
}

@article{habernal2014,
author = {Habernal, I. and Eckle-Kohler, Judith and Gurevych, I.},
year = {2014},
month = {01},
pages = {},
title = {Argumentation mining on the web from information seeking perspective},
volume = {1341},
journal = {CEUR Workshop Proceedings}
}



@article{macintosh2009,
    author = { Ann   Macintosh  and  Thomas F.   Gordon  and  Alastair   Renton },
    title = {Providing Argument Support for E-Participation},
    journal = {Journal of Information Technology \& Politics},
    volume = {6},
    number = {1},
    pages = {43-59},
    year  = {2009},
    publisher = {Routledge},
    doi = {10.1080/19331680802662113},
    
    URL = { 
            https://doi.org/10.1080/19331680802662113
        
    },
    eprint = { 
            https://doi.org/10.1080/19331680802662113
    }
}

@article{reed2004araucaria,
  title={Araucaria: Software for argument analysis, diagramming and representation},
  author={Reed, Chris and Rowe, Glenn},
  journal={International Journal on Artificial Intelligence Tools},
  volume={13},
  number={04},
  pages={961--979},
  year={2004},
  publisher={World Scientific}
}

@article{ATKINSON20181,
title = {Taking account of the actions of others in value-based reasoning},
journal = {Artificial Intelligence},
volume = {254},
pages = {1-20},
year = {2018},
issn = {0004-3702},
doi = {https://doi.org/10.1016/j.artint.2017.09.002},
url = {https://www.sciencedirect.com/science/article/pii/S0004370217301078},
author = {Katie Atkinson and Trevor Bench-Capon},
keywords = {Value-based reasoning, Practical reasoning, Expected utility, Argumentation schemes, Ultimatum Game, Prisoner's Dilemma}
}

@inproceedings{kokciyan2018,
author = {Kökciyan, Nadin and Sassoon, Isabel and Young, Peter and Chapman, Martin and Porat, Talya and Ashworth, Mark and Curcin, Vasa and Modgil, Sanjay and Parsons, Simon and Sklar, Elizabeth},
year = {2018},
month = {01},
pages = {},
title = {Towards an Argumentation System for Supporting Patients in Self-Managing their Chronic Conditions}
}

@inproceedings{Wagemans2016ConstructingAP,
  title={Constructing a Periodic Table of Arguments},
  author={Jean H. M. Wagemans},
  year={2016}
}

Schut, D., & Wagemans, J. H. M. (2014). Argumentatie en debat. Den Haag: Boom Lemma

@inproceedings{SchutWagemans,
  title={Argumentatie en debat},
  author={Schut, D., & Wagemans},
  year={2014}
}


@inproceedings{lindahl-etal-2019-towards,
    title = "Towards Assessing Argumentation Annotation - A First Step",
    author = "Lindahl, Anna  and
      Borin, Lars  and
      Rouces, Jacobo",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W19-4520",
    doi = "10.18653/v1/W19-4520",
    pages = "177--186",
}

@inproceedings{racismisavirus,
author = {Ziems, Caleb and He, Bing and Soni, Sandeep and Kumar, Srijan},
year = {2020},
month = {05},
pages = {},
title = {Racism is a Virus: Anti-Asian Hate and Counterhate in Social Media during the COVID-19 Crisis}
}

@inproceedings{musi2016,
author = {Musi, Elena and Ghosh, Debanjan and Muresan, Smaranda},
year = {2016},
month = {08},
pages = {},
title = {Towards Feasible Guidelines for the Annotation of Argument Schemes},
doi = {10.18653/v1/W16-2810}
}


@article{basile-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter",
    author = "Basile, Valerio  and
      Bosco, Cristina  and
      Fersini, Elisabetta  and
      Nozza, Debora  and
      Patti, Viviana  and
      Rangel Pardo, Francisco Manuel  and
      Rosso, Paolo  and
      Sanguinetti, Manuela",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S19-2007",
    doi = "10.18653/v1/S19-2007",
    pages = "54--63",
    abstract = "The paper describes the organization of the SemEval 2019 Task 5 about the detection of hate speech against immigrants and women in Spanish and English messages extracted from Twitter. The task is organized in two related classification subtasks: a main binary subtask for detecting the presence of hate speech, and a finer-grained one devoted to identifying further features in hateful contents such as the aggressive attitude and the target harassed, to distinguish if the incitement is against an individual rather than a group. HatEval has been one of the most popular tasks in SemEval-2019 with a total of 108 submitted runs for Subtask A and 70 runs for Subtask B, from a total of 74 different teams. Data provided for the task are described by showing how they have been collected and annotated. Moreover, the paper provides an analysis and discussion about the participant systems and the results they achieved in both subtasks.",
}

@article{cohen1960,
  added-at = {2008-03-03T18:54:52.000+0100},
  author = {Cohen, J.},
  biburl = {https://www.bibsonomy.org/bibtex/27e9a8dc8c720dcea2bda1cdb7d74b854/mstrohm},
  description = {Introduces Kappa as a way of calculating inter rater agreement between two raters},
  interhash = {83c003e853b90a89d55f97f50ad5ca7d},
  intrahash = {7e9a8dc8c720dcea2bda1cdb7d74b854},
  journal = {Educational and Psychological Measurement},
  keywords = {INFLUENTIAL methods tools},
  number = 1,
  pages = 37,
  timestamp = {2008-03-03T18:54:52.000+0100},
  title = {{A Coefficient of Agreement for Nominal Scales}},
  volume = 20,
  year = 1960
}

@inproceedings{stenetorp-etal-2012-brat,
    title = "brat: a Web-based Tool for {NLP}-Assisted Text Annotation",
    author = "Stenetorp, Pontus  and
      Pyysalo, Sampo  and
      Topi{\'c}, Goran  and
      Ohta, Tomoko  and
      Ananiadou, Sophia  and
      Tsujii, Jun{'}ichi",
    booktitle = "Proceedings of the Demonstrations at the 13th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2012",
    address = "Avignon, France",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/E12-2021",
    pages = "102--107",
}

@article{10.1162/coli_a_00364,
    author = {Lawrence, John and Reed, Chris},
    title = "{Argument Mining: A Survey}",
    journal = {Computational Linguistics},
    volume = {45},
    number = {4},
    pages = {765-818},
    year = {2020},
    month = {01},
    abstract = "{Argument mining is the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in natural language. Understanding argumentative structure makes it possible to determine not only what positions people are adopting, but also why they hold the opinions they do, providing valuable insights in domains as diverse as financial market prediction and public relations. This survey explores the techniques that establish the foundations for argument mining, provides a review of recent advances in argument mining techniques, and discusses the challenges faced in automatically extracting a deeper understanding of reasoning expressed in language in general.}",
    issn = {0891-2017},
    doi = {10.1162/coli_a_00364},
    url = {https://doi.org/10.1162/coli\_a\_00364},
    eprint = {https://direct.mit.edu/coli/article-pdf/45/4/765/1847520/coli\_a\_00364.pdf},
}

@inproceedings{bosc-etal-2016-dart,
    title = "{DART}: a Dataset of Arguments and their Relations on {T}witter",
    author = "Bosc, Tom  and
      Cabrio, Elena  and
      Villata, Serena",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://aclanthology.org/L16-1200",
    pages = "1258--1263",
    abstract = "The problem of understanding the stream of messages exchanged on social media such as Facebook and Twitter is becoming a major challenge for automated systems. The tremendous amount of data exchanged on these platforms as well as the specific form of language adopted by social media users constitute a new challenging context for existing argument mining techniques. In this paper, we describe a resource of natural language arguments called DART (Dataset of Arguments and their Relations on Twitter) where the complete argument mining pipeline over Twitter messages is considered: (i) we identify which tweets can be considered as arguments and which cannot, and (ii) we identify what is the relation, i.e., support or attack, linking such tweets to each other.",
}

@article{araucaria,
author = {Reed, Chris and Rowe, Glen},
year = {2004},
month = {12},
pages = {},
title = {Araucaria: Software for argument analysis, diagramming and representation},
volume = {14},
journal = {International Journal of AI Tools},
doi = {10.1142/S0218213004001922}
}

@article{rationale,
author = {van Gelder, Tim},
year = {2007},
month = {10},
pages = {23-42},
title = {The rationale for RationaleTM},
volume = {6},
journal = {Law, Probability and Risk},
doi = {10.1093/lpr/mgm032}
}

@article{Bex,
author = {Bex, Floris and Lawrence, John and Snaith, Mark and Reed, Chris},
year = {2013},
month = {10},
pages = {66-73},
title = {Implementing the Argument Web},
volume = {56},
journal = {Communications of the ACM},
doi = {10.1145/2500891}
}

@misc{tekiroglu2020guerini,
  doi = {10.48550/ARXIV.2004.04216},
  
  url = {https://arxiv.org/abs/2004.04216},
  
  author = {Tekiroglu, Serra Sinem and Chung, Yi-Ling and Guerini, Marco},
  
  keywords = {Computation and Language (cs.CL), Computers and Society (cs.CY), Social and Information Networks (cs.SI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Generating Counter Narratives against Online Hate Speech: Data and Strategies},
  
  publisher = {arXiv},
  
  year = {2020},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}


@misc{Benesch2014,
  author = {Benesch, Susan},
  url = {https://ssrn.com/abstract=3686876},
  title = {Countering Dangerous Speech: New Ideas for Genocide Prevention},
 
  year = {2014}
}

@ARTICLE{Dung95,
    author = {Phan Minh Dung},
    title = {On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games},
    journal = {Artificial Intelligence},
    year = {1995},
    volume = {77},
    pages = {321--357}
}

@book{rahwan2009argumentation,
  title={Argumentation in artificial intelligence},
  author={Rahwan, Iyad and Simari, Guillermo R},
  volume={47},
  year={2009},
  publisher={Springer}
}

@inproceedings{sutskever-et-al,
author = {Sutskever, Ilya and Vinyals, Oriol and Le, Quoc V.},
title = {Sequence to Sequence Learning with Neural Networks},
year = {2014},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
abstract = {Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous state of the art. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.},
booktitle = {Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2},
pages = {3104–3112},
numpages = {9},
location = {Montreal, Canada},
series = {NIPS'14}
}

@article{raffel-et-al,
author = {Raffel, Colin and Shazeer, Noam and Roberts, Adam and Lee, Katherine and Narang, Sharan and Matena, Michael and Zhou, Yanqi and Li, Wei and Liu, Peter J.},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
year = {2020},
issue_date = {January 2020},
publisher = {JMLR.org},
volume = {21},
number = {1},
issn = {1532-4435},
abstract = {Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pretraining objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code.},
journal = {J. Mach. Learn. Res.},
month = {jan},
articleno = {140},
numpages = {67},
keywords = {natural language processing, transfer learning, attention based models, multi-task learning, deep learning}
}

@inproceedings{devlin-etal-2019-bert,
    title = "{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding",
    author = "Devlin, Jacob  and
      Chang, Ming-Wei  and
      Lee, Kenton  and
      Toutanova, Kristina",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    doi = "10.18653/v1/N19-1423",
    pages = "4171--4186",
   
}



@Article{citron2011intermediaries,
  title={Intermediaries and hate speech: Fostering digital citizenship for our information age},
  author={Citron, Danielle Keats and Norton, Helen},
  journal={BUL Rev.},
  volume={91},
  pages={1435},
  year={2011},
  publisher={HeinOnline}
}


@misc{liu2019roberta,
  doi = {10.48550/ARXIV.1907.11692},
  
  url = {https://arxiv.org/abs/1907.11692},
  
  author = {Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin},
  
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {RoBERTa: A Robustly Optimized BERT Pretraining Approach},
  
  publisher = {arXiv},
  
  year = {2019},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@misc{perez2021robertuito,
      title={RoBERTuito: a pre-trained language model for social media text in Spanish},
      author={Juan Manuel Pérez and Damián A. Furman and Laura Alonso Alemany and Franco Luque},
      year={2021},
      eprint={2111.09453},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{nguyen-etal-2020-bertweet,
    title = "{BERT}weet: A pre-trained language model for {E}nglish Tweets",
    author = "Nguyen, Dat Quoc  and
      Vu, Thanh  and
      Tuan Nguyen, Anh",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-demos.2",
    doi = "10.18653/v1/2020.emnlp-demos.2",
    pages = "9--14",
    abstract = "We present BERTweet, the first public large-scale pre-trained language model for English Tweets. Our BERTweet, having the same architecture as BERT-base (Devlin et al., 2019), is trained using the RoBERTa pre-training procedure (Liu et al., 2019). Experiments show that BERTweet outperforms strong baselines RoBERTa-base and XLM-R-base (Conneau et al., 2020), producing better performance results than the previous state-of-the-art models on three Tweet NLP tasks: Part-of-speech tagging, Named-entity recognition and text classification. We release BERTweet under the MIT License to facilitate future research and applications on Tweet data. Our BERTweet is available at https://github.com/VinAIResearch/BERTweet",
}

@misc{devlin2018bert,
  doi = {10.48550/ARXIV.1810.04805},
  
  url = {https://arxiv.org/abs/1810.04805},
  
  author = {Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
  
  publisher = {arXiv},
  
  year = {2018},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}


@book{walton_reed_macagno_2008, place={Cambridge}, title={Argumentation Schemes}, DOI={10.1017/CBO9780511802034}, publisher={Cambridge University Press}, author={Walton, Douglas and Reed, Christopher and Macagno, Fabrizio}, year={2008}}

@inproceedings{kokciyan2018towards,
  title={Towards an argumentation system for supporting patients in self-managing their chronic conditions},
  author={Kokciyan, Nadin and Sassoon, Isabel and Young, Anthony P and Chapman, Martin and Porat, Talya and Ashworth, Mark and Curcin, Vasa and Modgil, Sanjay and Parsons, Simon and Sklar, Elizabeth},
  booktitle={Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence},
  year={2018}
}

@article{macintosh2009,
    author = { Ann   Macintosh  and  Thomas F.   Gordon  and  Alastair   Renton },
    title = {Providing Argument Support for E-Participation},
    journal = {Journal of Information Technology \& Politics},
    volume = {6},
    number = {1},
    pages = {43-59},
    year  = {2009},
    publisher = {Routledge},
    doi = {10.1080/19331680802662113},
    
    URL = { 
            https://doi.org/10.1080/19331680802662113
        
    },
    eprint = { 
            https://doi.org/10.1080/19331680802662113
    }
}

@article{reed2004araucaria,
  title={Araucaria: Software for argument analysis, diagramming and representation},
  author={Reed, Chris and Rowe, Glenn},
  journal={International Journal on Artificial Intelligence Tools},
  volume={13},
  number={04},
  pages={961--979},
  year={2004},
  publisher={World Scientific}
}


@article{fourArgumentForms2019,
author = {Wagemans, Jean},
year = {2019},
month = {03},
pages = {57-69},
title = {Four Basic Argument Forms},
volume = {17},
journal = {Research in Language},
doi = {10.2478/rela-2019-0005}
}

@inproceedings{lindahl-etal-2019-towards,
    title = "Towards Assessing Argumentation Annotation - A First Step",
    author = "Lindahl, Anna  and
      Borin, Lars  and
      Rouces, Jacobo",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W19-4520",
    doi = "10.18653/v1/W19-4520",
    pages = "177--186",
}

@inproceedings{musi2016,
author = {Musi, Elena and Ghosh, Debanjan and Muresan, Smaranda},
year = {2016},
month = {08},
pages = {},
title = {Towards Feasible Guidelines for the Annotation of Argument Schemes},
doi = {10.18653/v1/W16-2810}
}


@article{10.1162/coli_a_00364,
    author = {Lawrence, John and Reed, Chris},
    title = "{Argument Mining: A Survey}",
    journal = {Computational Linguistics},
    volume = {45},
    number = {4},
    pages = {765-818},
    year = {2020},
    month = {01},
    abstract = "{Argument mining is the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in natural language. Understanding argumentative structure makes it possible to determine not only what positions people are adopting, but also why they hold the opinions they do, providing valuable insights in domains as diverse as financial market prediction and public relations. This survey explores the techniques that establish the foundations for argument mining, provides a review of recent advances in argument mining techniques, and discusses the challenges faced in automatically extracting a deeper understanding of reasoning expressed in language in general.}",
    issn = {0891-2017},
    doi = {10.1162/coli_a_00364},
    url = {https://doi.org/10.1162/coli\_a\_00364},
    eprint = {https://direct.mit.edu/coli/article-pdf/45/4/765/1847520/coli\_a\_00364.pdf},
}


@article{araucaria,
author = {Reed, Chris and Rowe, Glen},
year = {2004},
month = {12},
pages = {},
title = {Araucaria: Software for argument analysis, diagramming and representation},
volume = {14},
journal = {International Journal of AI Tools},
doi = {10.1142/S0218213004001922}
}

@article{rationale,
author = {van Gelder, Tim},
year = {2007},
month = {10},
pages = {23-42},
title = {The rationale for RationaleTM},
volume = {6},
journal = {Law, Probability and Risk},
doi = {10.1093/lpr/mgm032}
}

@article{Bex,
author = {Bex, Floris and Lawrence, John and Snaith, Mark and Reed, Chris},
year = {2013},
month = {10},
pages = {66-73},
title = {Implementing the Argument Web},
volume = {56},
journal = {Communications of the ACM},
doi = {10.1145/2500891}
}

@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {The Theory of Parsing, Translation and Compiling},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}

@book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
    publisher = {American Psychological Association},
    address = {Washington, DC}
}

@article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = "1981",
	title = {Alternation},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",
	number = "1",
	pages = "114--133",
	doi = "10.1145/322234.322243",
}

@inproceedings{andrew2007scalable,
  title={Scalable training of {L1}-regularized log-linear models},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={Proceedings of the 24th International Conference on Machine Learning},
  pages={33--40},
  year={2007},
}

@book{Gusfield:97,
    author  = {Dan Gusfield},
    title   = {Algorithms on Strings, Trees and Sequences},
    year    = "1997",
    publisher = {Cambridge University Press},
    address = {Cambridge, UK}
}

@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}
}


% @book{Aho:72,
%     author  = {Alfred V. Aho and Jeffrey D. Ullman},
%     title   = {The Theory of Parsing, Translation and Compiling},
%     year    = "1972",
%     volume  = "1",
%     publisher = {Prentice-Hall},
%     address = {Englewood Cliffs, NJ}
% }

% @book{APA:83,
%     author  = {{American Psychological Association}},
%     title   = {Publications Manual},
%     year    = "1983",
%    publisher = {American Psychological Association},
%    address = {Washington, DC}
% }

% @article{Chandra:81,
% 	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
% 	year = "1981",
% 	title = {Alternation},
% 	journal = {Journal of the Association for Computing Machinery},
% 	volume = "28",
% 	number = "1",
% 	pages = "114--133",
% 	doi = "10.1145/322234.322243",
% }

% @inproceedings{andrew2007scalable,
%   title={Scalable training of {L1}-regularized log-linear models},
%   author={Andrew, Galen and Gao, Jianfeng},
%   booktitle={Proceedings of the 24th International Conference on Machine Learning},
%   pages={33--40},
%   year={2007},
% }

% @book{Gusfield:97,
%     author  = {Dan Gusfield},
%     title   = {Algorithms on Strings, Trees and Sequences},
%     year    = "1997",
%     publisher = {Cambridge University Press},
%     address = {Cambridge, UK}
% }

% @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}}
