Base_JSON/prefixI/json/isa/2021.isa-1.5.json

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    "paper_id": "2021",
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        "date_generated": "2023-01-19T07:12:27.457852Z"
    },
    "title": "Discourse-based Argument Segmentation and Annotation",
    "authors": [
        {
            "first": "Ekaterina",
            "middle": [],
            "last": "Saveleva",
            "suffix": "",
            "affiliation": {
                "laboratory": "Spoken Language Systems Group",
                "institution": "Saarland Informatics Campus Saarland University",
                "location": {
                    "settlement": "Saarbr\u00fccken",
                    "country": "Germany"
                }
            },
            "email": "esaveleva@lsv.uni-saarland.de"
        },
        {
            "first": "Volha",
            "middle": [],
            "last": "Petukhova",
            "suffix": "",
            "affiliation": {
                "laboratory": "Spoken Language Systems Group",
                "institution": "Saarland Informatics Campus Saarland University",
                "location": {
                    "settlement": "Saarbr\u00fccken",
                    "country": "Germany"
                }
            },
            "email": "vpetukhova@lsv.uni-saarland.de"
        },
        {
            "first": "Marius",
            "middle": [],
            "last": "Mosbach",
            "suffix": "",
            "affiliation": {
                "laboratory": "Spoken Language Systems Group",
                "institution": "Saarland Informatics Campus Saarland University",
                "location": {
                    "settlement": "Saarbr\u00fccken",
                    "country": "Germany"
                }
            },
            "email": "mmosbach@lsv.uni-saarland.de"
        },
        {
            "first": "Dietrich",
            "middle": [],
            "last": "Klakow",
            "suffix": "",
            "affiliation": {
                "laboratory": "Spoken Language Systems Group",
                "institution": "Saarland Informatics Campus Saarland University",
                "location": {
                    "settlement": "Saarbr\u00fccken",
                    "country": "Germany"
                }
            },
            "email": "dklakow@lsv.uni-saarland.de"
        }
    ],
    "year": "",
    "venue": null,
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    "abstract": "The paper presents a discourse-based approach to the analysis of argumentative texts based on the assumption that the coherence of a text should capture argumentation structure. Therefore, existing discourse analysis tools can be successfully applied for argument segmentation and annotation tasks. We tested widely used Penn Discourse Tree Bank parser (Lin et al., 2010) and the state-of-the-art neural network NeuralEDUSeg (Wang et al., 2018) and XLNet (Yang et al., 2019) models on discourse segmentation and discourse relation recognition tasks. The two-stage approach outperformed the PDTB parser by broad margin, i.e. the best achieved F1 scores of 21.2% for PDTB parser vs 66.37% for NeuralEDUSeg and XLNet models. Neural network models were fine-tuned and evaluated on the argumentative corpus showing a promising accuracy of 60.22%. The complete argument structures were reconstructed for further argumentation mining tasks. The reference Dagstuhl argumentative corpus containing 2,222 elementary discourse unit pairs annotated with the toplevel and fine-grained PDTB relations will be released to the research community.",
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            {
                "text": "The paper presents a discourse-based approach to the analysis of argumentative texts based on the assumption that the coherence of a text should capture argumentation structure. Therefore, existing discourse analysis tools can be successfully applied for argument segmentation and annotation tasks. We tested widely used Penn Discourse Tree Bank parser (Lin et al., 2010) and the state-of-the-art neural network NeuralEDUSeg (Wang et al., 2018) and XLNet (Yang et al., 2019) models on discourse segmentation and discourse relation recognition tasks. The two-stage approach outperformed the PDTB parser by broad margin, i.e. the best achieved F1 scores of 21.2% for PDTB parser vs 66.37% for NeuralEDUSeg and XLNet models. Neural network models were fine-tuned and evaluated on the argumentative corpus showing a promising accuracy of 60.22%. The complete argument structures were reconstructed for further argumentation mining tasks. The reference Dagstuhl argumentative corpus containing 2,222 elementary discourse unit pairs annotated with the toplevel and fine-grained PDTB relations will be released to the research community.",
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                "section": "Abstract",
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                "text": "Enormous and ever growing digital content provides information where opinions, sentiment and arguments can be identified and analysed. For example, news and social media content is searched to filter or weight the validity of statements (Rowe and Butters, 2009) , to identify the presence of fake news and false claims (Popat et al., 2018) , to analyse opinions in public discussions (Murakami and Raymond, 2010) , to detect opinion manipulation (Cambria et al., 2010) , to predict consumers sentiment (Bai, 2011) , to study citizen engagement (Purpura et al., 2008) , and to recognize stance in political online debates (Somasundaran and Wiebe, 2010; Walker et al., 2012) . Arguments from legal (Moens et al., 2007) , financial (Hogenboom et al., 2010) or medical (Sanchez Graillet and Cimiano, 2019) documents are extracted to support professional decision-making. Natural argumentation is the focus of numerous educational scenarios assessing student's essays quality and training argumentation and debate skills (Ashley et al., 2007; Petukhova et al., 2017) . Automatic extraction and analysis of arguments from heterogeneous data is one of the important tasks of argumentation mining which aims to provide structured data for computational models of argument and reasoning engines (Lippi and Torroni, 2016) .",
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                        "text": "(Rowe and Butters, 2009)",
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                        "text": "(Popat et al., 2018)",
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                        "start": 384,
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                        "text": "(Murakami and Raymond, 2010)",
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                        "start": 446,
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                        "text": "(Cambria et al., 2010)",
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                        "start": 502,
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                        "text": "(Bai, 2011)",
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                        "text": "(Purpura et al., 2008)",
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                        "text": "(Somasundaran and Wiebe, 2010;",
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                        "text": "Walker et al., 2012)",
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                        "text": "(Moens et al., 2007)",
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                        "text": "(Hogenboom et al., 2010)",
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                        "text": "(Sanchez Graillet and Cimiano, 2019)",
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                "section": "Introduction",
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                "text": "While for some applications, an argument can be considered as an atomic entity without internal structure, for others defining its structure becomes crucial. For example, to recognize the speaker 'stance' 1 in online debates, the whole post can be acknowledged as an argument in 'favour' or 'against' a certain motion. An argument is, therefore, analysed given the other supporting or attacking arguments (Dung, 1995) . Other argumentation mining tasks require structured argumentation models, e.g. tasks that aim at understanding and emulation of human inference, investigating patterns of reasoning, and tasks that focus on extraction and validity assessment of arguments.",
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                "text": "Identification and classification of argument components are rather challenging tasks . The argument definition, the description of elementary units and building blocks of an argument, relations between and inside these units, the argument structures and argumentation schemes are still under debate. A simple argument structure is often considered as consisting of a claim that is supported by evidence (Mochales and Moens, 2011; . A claim is an assertion that the argument aims to prove, i.e. a claim is a conclusion whose merit must be estab- lished. Evidence presents (a set of) proposition(-s) which provide grounds for drawing the conclusion. Automatic recognition of relevant semantic units involves two tasks: (1) segmentation of a text into meaningful units; and (2) annotation of these units capturing (part of) their meaning. Many argumentation mining studies assume that the boundaries of the argument components have been previously detected by other means, thus they focus on the classification task (Stab and Gurevych, 2014; Eckle-Kohler et al., 2015) . Other consider segmentation as a sub-task and perform both segmentation and classification Rinott et al., 2015) .",
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                "section": "Introduction",
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                "text": "We define argument structure recognition to involve: (1) segmentation of a text into elementary argumentative units assuming that they correspond to elementary discourse units; (2) discourse relation detection between them; (3) classification of the identified relations; (4) classification of the identified argumentative units based on the classified discourse relations; and (5) argument completionreconstruction of implicit units to achieve a complete argument structure, see also (Peldszus and Stede, 2013) . In this study we evaluate state-of-theart discourse parsers and machine learning models on automatic segmentation and discourse relation classification tasks, and then apply them to extract arguments from argumentative texts.",
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                "section": "Introduction",
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                "text": "The rest of the paper is structured as follows. In Section 2, we discuss related work concerning argument structure recognition. Section 3 presents established discourse theories as theoretical and empirical framework for argument analysis and argumentation modelling. The connection to the existing ISO 24617-8 standard for discourse relation annotation is made. Section 4 discusses the performed experiments elaborating on the datasets, tools and outcomes. Section 5 summarizes the results and outlines the future research.",
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                "section": "Introduction",
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                "text": "Peldszus and Stede (2013) defined Argumentative Discourse Units (ADUs) as text segments corresponding to propositions that are argumentatively relevant and have their own argumentative function. ADUs reflect different ways to support a claim (Fig. 1 ), e.g. with the basic argument configuration consisting of a conclusion supported by exactly one premise, as in example (1) below. If there are multiple premises supporting a conclusion together, the structure is called linked support as in (2). Multiple premises which support the conclusion independently form a multiple support as in (3). Serial support links arguments to the conclusion where an argument contributes to further development of an already given argument (4). Peldszus and Stede (2013) consider the example shown in (5) to be a special form of support.",
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                        "text": "(Fig. 1",
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            {
                "text": "(1) [Books are better than TV.]1 [Books enlighten the soul.]2",
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                "text": "(2) [ Since not every text is argumentative and, therefore, subjected to an argumentative analysis, identification of its type can be considered as a preliminary step, and together with the topic context may provide valuable information for the argument component identification. introduced the notion of a context-dependent claim -a general concise statement that directly supports or contests a given topic. Rinott et al. (2015) detect contextdependent evidence -text segments that directly support a claim in the context of a given topic. Contextual information has served as an important source for argument component identification in Mining arguments from diverse corpora based on topic can pose certain problems. A well-established topic is not always easy to determine or a text can cover several topics and the discussion can shift between them throughout the entire text. proposed a method for contextindependent claim detection. The approach relies on the assumption that argumentative sentences share the structure independently of the addressed topic. This technique was successfully applied for legal texts , clinical trials (Mayer et al., 2018) and social media (Liga, 2019) .",
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                "text": "Cross-domain approach to the argumentation mining has been explored in a number of studies. Rosenthal and McKeown (2012) detect claims from two different data sets, LiveJournal and Wikipedia. Al Khatib et al. (2016) experimented with a wider range of text types and topics addressing politics, culture, religion, sport, economy, and health. Deep learning techniques were applied in cross-domain and multi-task learning scenarios Stab et al., 2018; Schulz et al., 2018; Morio and Fujita, 2019; Mensonides et al., 2019; Wambsganss et al., 2020) .",
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                        "text": "Rosenthal and McKeown (2012)",
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                "text": "Argument structure is often viewed through the prism of discourse theory and ADU components are defined based on discourse units which proves that argumentation and discourse characteristics, and these structures are closely related. Peldszus and Stede (2016) explored the mapping between discourse and argument(-ation) structures based on the Rhetorical Structure Theory (RST, Mann and Thompson (1988) ) and those of Segmented Discourse Representation Theory (SDRT, Lascarides and Asher (2008) ). assesses the role of discourse parsing features for argumentation structure prediction. Cabrio et al. (2013) and Hewett et al. (2019) translated the general sim-ple argument structure into several discourse-based schemes to perform analysis and evaluation of natural language arguments, see also (Teufel et al., 1999; Palau and Moens, 2009; Petukhova et al., 2017) . Eckle-Kohler et al. (2015) assessed the role of discourse markers for claims and evidence detection. In Hofmockel et al. (2017) , the impact of the genre on different realizations of discourse relations is evaluated. Green (2018) applied the genre-based approach to scientific (e.g. biological/biomedical) texts.",
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                        "text": "(RST, Mann and Thompson (1988)",
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                "text": "Discourse theory aims at explaining the coherence of a text. Its central notion is coherence, also called rhetorical or discourse relation -a semantic or pragmatic relation between two adjacent text spans. Even though text coherence and argumentation structure are not identical, discourse structure can reveal new unexplored properties of argumentation. Bridging from discourse to argumentation, Peldszus and Stede (2013) chooses the RST framework where all parts of a text are involved into a discourse structure and organized as a tree, with Elementary Discourse Units (EDUs) as leaves. An EDU is a minimal building block of a discourse tree which typically corresponds to a clause (Carlson and Marcu, 2001 ). 2 RST specifies how EDUs and larger units are connected, where some text spans are more important than the others, i.e. nucleus or multiple nuclea are the central part of a relation in the text supported by a satellite. The corresponding RST tagset contains 78 discourse relations which can be grouped into 16 classes sharing one type of rhetorical meaning.",
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                "section": "Discourse Analysis",
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                "text": "Another influential discourse analysis frame-work is defined within Penn Discourse Tree Bank (PDTB, Prasad et al. (2005) ). PDTB does not make strong assumptions about the overall structure of a text and does not suggest what kinds of high-level structures may be created from the annotated lowlevel relations and arguments. The PDTB analysis is focused on the discourse relation between two text segments called Arg1 and Arg2 which can be treated as EDUs. PDTB accounts for the lexical items that can signal discourse relations -discourse connectives. In the case of explicit connectives, Arg2 is the argument to which the connective is syntactically bound, and Arg1 is the other argument. In the case of relations between adjacent sentences, Arg1 and Arg2 reflect the linear order of the arguments, with Arg1 before Arg2. PDTB does not constrain an EDU to be a single clause or single sentence, however, the framework follows a minimality principle requiring an argument to contain the minimal amount of information needed to interpret the relation successfully. The PDTB annotation scheme forms the basis of the ISO DR-Core (ISO 24617-8) discourse relations annotation standard (Bunt and Prasad, 2016) .",
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                "text": "Even though RST and PDTB annotation frameworks make different assumptions about the discourse structure and define different sets of relations, Demberg et al. (2019) suggest an automatic alignment of their relations and evaluates the mapping discrepancies. Discourse analysis within both annotation frameworks includes (1) segmentation of the text into EDUs; and (2) the recognition of discourse relations between these units. Discourse parsers typically perform both tasks. For example, Lin et al. (2010) designed a full parser to perform the PDTB annotations. The system first identifies discourse connectives, label the corresponding Arg1 and Arg2 spans and assign an Explicit relation. If no connective was identified, the system classifies the statement pair as having one of the other relation types, i.e. Implicit, EntRel, AltLex, NoRel.",
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                "text": "Wang and Lan (2015) extended the parser with extractors for Arg1, Arg2 and Non-EntRel relations. Qin et al. (2016) improved recognition of the implicit relations. Recent works explore deep learning techniques which use architectures for multi-task learning (Liu et al., 2016; Lan et al., 2017; Van Ngo et al., 2019) or adversarial neural networks (Qin et al., 2017; Huang and Li, 2019) .",
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                "text": "While many studies focus exclusively on the discourse relation recognition assuming that the text is already pre-segmented, others also consider discourse segmentation task. Early generation segmenters were rule-based systems (LeThanh et al., 2004; Tofiloski et al., 2009) , whereas more recent approaches view this task as sequence labeling problem and use deep learning (Hernault et al., 2010; Bach et al., 2012; Wang et al., 2018) . Multilingual discourse segmentation is addressed in (Braud et al., 2017; Muller et al., 2019; Desai et al., 2020) .",
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                        "text": "Desai et al., 2020)",
                        "ref_id": "BIBREF15"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Discourse Analysis",
                "sec_num": "3"
            },
            {
                "text": "The presented study concerns both segmentation and classification tasks assessing the performance of the state-of-the-art tools on the argumentative corpus. Design and results are reported in the next Section.",
                "cite_spans": [],
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                "section": "Discourse Analysis",
                "sec_num": "3"
            },
            {
                "text": "We conducted the following experiments: (1) evaluating the quality of the existing full discourse parsers on EDUs segmentation and relation classification tasks; (2) two-stage discourse segmentation and relation annotation; (3) application and evaluation of the best performing model to identify and classify argument components in the argumentative corpus; and (4) completion of argument structure by reconstructing implicit claims. Figure 3 shows the experimental workflow.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 434,
                        "end": 442,
                        "text": "Figure 3",
                        "ref_id": "FIGREF3"
                    }
                ],
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                "section": "Experimental Design",
                "sec_num": "4"
            },
            {
                "text": "There are two corpora used in this study: Penn Discourse Treebank 2.0 (PDTB 2.0, Prasad et al. (2008)) 3 -a large scale corpus annotated with information related to discourse structure and discourse semantics, and Dagstuhl15512 ArgQuality (Wachsmuth et al., 2017) -a corpus of segmented arguments annotated with argument quality scores. PDTB 2.0 consists of 2,159 articles from Wall Street Journal (WSJ) divided into 25 sections. In total, there are 40600 discourse unit pairs annotated with different relations. We provide a list of relations and their distribution in the Appendix. Dagstuhl15512 ArgQuality (Wachsmuth et al., 2017 ) is a collection of argumentative texts from the UKPConvArgRank dataset (Habernal and Gurevych, 2016) consisting of debate portal arguments for and against stances on 16 topics. The UKPConvArgRank dataset was developed to predict the convincingness of arguments, so that each argument pair is rated as more or less convincing. For Dagstuhl15512 ArgQuality, texts on each topic containing the five top-and five bottom-ranked arguments were selected and annotated across three core quality dimensions: argument cogency, argument effectiveness and argument reasonableness, and several sub-criteria (15 in total).",
                "cite_spans": [
                    {
                        "start": 609,
                        "end": 632,
                        "text": "(Wachsmuth et al., 2017",
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                        "start": 706,
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                        "text": "(Habernal and Gurevych, 2016)",
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                ],
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                "section": "Datasets",
                "sec_num": "4.1"
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                "text": "One of the widely used tools for discourse processing is the PDTB parser developed by Lin et al. (2010) . It is trained on sections 02-21 of Penn Discourse Tree Bank (PDTB 1.0, Prasad et al. (2005) ) for text span identification and relation classification. For our purposes, spans for both Arg1 and Arg2 need to correspond exactly or partially to the PDTB 2.0 reference segments. Moreover, the relation between EDUs should be correctly classified. We evaluated the parser performance on the full PDTB 2.0 corpus. Table 1 summarizes parser performance in terms of F1 scores. The gold standard parsing and EDUs boundaries with error propagation setting (GS + EP ) refers to a clean, per-component evaluation. In the automatic parsing and EDUs boundaries with error propagation scenario (Auto + EP ), end-to-end automated parsing of the unseen data is performed. In the later setting, F1 scores of 38.18% and 20.64% were achieved for partial and exact match, respectively. A large portion of the misclassified cases belong to the Non-Explicit classes, as implicit discourse relations are more difficult to classify. The bottom part of Table 1 reports F1 scores obtained on the EDU span identification and on the joint segmentation and classification tasks on the entire PDTB 2.0 corpus.",
                "cite_spans": [
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                        "start": 86,
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                        "text": "Lin et al. (2010)",
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                        "start": 177,
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                        "text": "Prasad et al. (2005)",
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                        "start": 514,
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                "sec_num": "4.2.1"
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            {
                "text": "Similarly to Hewett et al. (2019) , we observed that the parser failed to identify many spans correctly. In case of the correct span identification, relation classification was reasonably accurate. Figure 4 shows the confusion matrix for the top-level (L1) relations between the correctly identified pairs of Arg1 and Arg2. We concluded that the parser generally tends to assign a relation between the majority of EDU spans misclassifying NoRel instances.",
                "cite_spans": [
                    {
                        "start": 13,
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                        "text": "Hewett et al. (2019)",
                        "ref_id": "BIBREF22"
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                    {
                        "start": 198,
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                        "text": "Figure 4",
                        "ref_id": "FIGREF4"
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                "section": "Discourse Parsing",
                "sec_num": "4.2.1"
            },
            {
                "text": "As shown in the parser evaluation experiments, EDU segmentation is a crucial step in discourse analysis. based model NeuralEDUSeg developed by Wang et al. (2018) . In this experiment, a unit is acknowledged to be correctly segmented if it partially or fully corresponds to one of the reference PDTB segments. Table 2 reports the number of exact and partial matches. Segmentation performance achieves 68.55% of F1 score. Our results show that NeuralE-DUSeg significantly outperforms the PDTB parser (compare with Table 1 ). While the number of exact matches is still rather low (10.84%), we observed a relatively high number of identified partial matches (45.92%). The fact that most matches coincide with the reference segmentation only partially can be explained by the fact that NeuralEDUSeg is originally trained on the RST-DT corpus which follows different segmentation principles (consider Figure 2 again) . Minimal RST-DT units tend to be shorter than those of the PDTB. Deep learning models show promising results on discourse relation recognition task. Kim et al. (2020) demonstrated that the XLNet-large model of Yang et al. (2019) achieved the best results on implicit discourse relation recognition significantly outperforming BERT- (Nie et al., 2019) and ELMObased (Bai et al., 2019) discourse relations models. We performed a series of experiments on finetuning XLNet for the discourse relation recognition task. We first conducted a binary classification to establish whether is any relation between the identified units, i.e. the model discriminates between Rel class (includes any type of discourse relations) and NoRel comprising the EntRel and NoRel types. Secondly, we performed five-class top-level (L1) and ten-class fine-grained (L2) relations classification. The following five classes were used for the second experiment: Expansion, Conjunction, Comparison, Contingency, Temporal, NoRel. The ten-class experiment exploited the classes listed below: Expansion.Conjunction, Expansion.Restatement, Expansion.Instantiation, Temporal.Synchrony, Temporal.Asynchronous, Contingency.Cause, Contingency.Condition, Comparison.Contrast, Comparison.Concession. See Appendix for the class distribution. Classes with less than 500 training instances were excluded. The training set comprised sections 0-21 of the PDTB 2.0 corpus; sections 22-24 served as the test set. Since classes were not balanced in all classification settings, we performed re-sampling procedure: up-sampling of the underrepresented NoRel class in binary classification by adding synthetic samples combining random EDUs from different textual units; and down-sampling the majority classes in the multi-class settings. The right part of Table 2 presents the final training and test data partitions for each classification scenario.",
                "cite_spans": [
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                        "start": 143,
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                        "text": "Wang et al. (2018)",
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                        "text": "Kim et al. (2020)",
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                        "text": "(Nie et al., 2019)",
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                        "start": 895,
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                        "text": "Figure 2 again)",
                        "ref_id": "FIGREF1"
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                ],
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                "section": "Discourse Segmentation and Relation Recognition",
                "sec_num": "4.2.2"
            },
            {
                "text": "For the training and evaluation procedure, we fine-tuned each encoder model following the suggestions of Mosbach et al. (2021) and trained for 10 epochs using a learning rate of 0.00001 and a batch-size of eight. The results are summarized in Table 2 , from which we can observe that accuracy drops with a higher number of classes to learn, from 88.85% for two classes to 53% for ten classes. We note that the results of our experiments differ from those reported by Kim et al. (2020) due to the differences in the approach and set of the classified relations. For instance, we were not focused on the distinction between implicit vs. explicit relation recognition. The goal was to assess how well the model predicts cases when a relation between two segments exists without focusing on how this relation is expressed. Moreover, we included NoRel instances into the classification, while they are typically discarded in other studies.",
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                        "start": 105,
                        "end": 126,
                        "text": "Mosbach et al. (2021)",
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                        "start": 467,
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                        "text": "Kim et al. (2020)",
                        "ref_id": "BIBREF26"
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                        "start": 243,
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            {
                "text": "We applied the tested discourse analysis tools on Dagstuhl15512 ArgQuality corpus where we manually examined and corrected the model outputs. Respecting the PDTB minimality principle, we combined or split relevant text units depending on the amount of information required to interpret the relation between the segments correctly. We conduct a detailed error analysis and discuss some representative cases below. We encountered many examples where a single unit does not contain substantial semantic information and has to be combined with the adjacent segment(-s) as illustrated in 7 We considered modal constructions such as I think, I believe, I am sure, Maybe, I highly doubt as in (8), infinitive constructions (9), participle constructions (10) and relative clauses 11 We also encountered a few cases where the segment identified by the parser can be split into several EDUs as in ( Table 3 : Performance on EDU segmentation task applying NeuralEDUSeg model Wang et al. (2018) on the Dagstuhl corpus in terms of F1 scores (in %); and accuracy scores (in %) for 5-and 10 class discourse relation classification on the DagStuhl corpus with the fine-tuned XLNet-large model. Table 3 reports the performance of the NeuralE-DUSeg model evaluated on the manually segmented argumentative Dagstuhl corpus using the reference segmentation.",
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                "section": "Discourse-based Analysis of an Argumentative Corpus",
                "sec_num": "4.3"
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                "text": "As the next step, the identified EDUs were used to classify discourse relation between them applying XLNet (Yang et al., 2019) . For this, pairs of adjacent segments were constructed and annotated with the PDTB discourse relations. We focused on ten classes mentioned above (the distribution is provided in the Appendix). Wachsmuth et al. (2017) notes that some argument components, most often a claim, can be implicit. Consider an example in (13) below. An argument is not complete without the claim and cannot be used for further argumentation mining tasks. Therefore, we reconstructed a claim for every topic in the corpus, i.e. either 'for' or 'against' stance it may present. The reconstructed claim is a simple sentence which correspond to a single EDU. Subsequently, the reconstructed claims were used to created EDUs pairs for discourse relation classification.",
                "cite_spans": [
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                        "start": 107,
                        "end": 126,
                        "text": "(Yang et al., 2019)",
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                        "start": 322,
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                        "text": "Wachsmuth et al. (2017)",
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                "section": "Discourse-based Analysis of an Argumentative Corpus",
                "sec_num": "4.3"
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            {
                "text": "(13) (a) The question is: who has the right to prohibit it? Government? Why would there be any pressing need at all for the state to outlaw pornography? Look at Europethey're cool with pretty much everything. I don't see any moral depravity in Europe, do you? (implicit claim: Pornography is not wrong. ) (b) Books will be always great whatever the new technological developments emerges, books has its fixed place in every humans heart. (implicit claim: Books are better than TV.) EDUs pairs were built considering non-adjacent text units connected by a discourse relation. Most frequently, a claim may be connected to segments representing various types of evidence at different support levels as in 14 The resulting Dagstuhl corpus annotated with discourse relations contains the same number of 304 arguments as the original one which are segmented into 2,222 EDUs pairs. The XLNet-large model, initially trained and fine-tuned on the PDTB 2.0 dataset, was evaluated on Dagstuhl, see Table 3 for the performance overview. Figure 5 presents the confusion matrix for the 5-class relation classification task. We observed that many relations are correctly classified even in the absence of discourse connectives on which the model relies. Consider the following classification output: Physical education does absolutely nothing for the children 's health and/or lifestyle . Expansion.Instantiation Let me describe my PE experience. Throughout my public education career , PE has been mandatory for each year.",
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                        "end": 994,
                        "text": "Table 3",
                        "ref_id": null
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                        "end": 1033,
                        "text": "Figure 5",
                        "ref_id": "FIGREF5"
                    }
                ],
                "eq_spans": [],
                "section": "Discourse-based Analysis of an Argumentative Corpus",
                "sec_num": "4.3"
            },
            {
                "text": "I think common good is better than personal pursuit Comparison.Contrast Yes personal pursuit is important. it wouldn't be so easily for you to become fat Contingency.Condition (of course you would also need to keep a balanced diet)",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Discourse-based Analysis of an Argumentative Corpus",
                "sec_num": "4.3"
            },
            {
                "text": "To summarize, the evaluated discourse processing tools showed a reasonable segmentation (F1 score ranging from 47.94% for exact match to 79.83% for partial match) and discourse relation recognition (accuracy ranging from 50.48% to 60.22%) performance on argumentative data. Thus, they can be applied in argument structure recognition and reconstruction tasks.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Discourse-based Analysis of an Argumentative Corpus",
                "sec_num": "4.3"
            },
            {
                "text": "The presented study reviewed discourse-based approaches to argumentative discourse analysis. We evaluated three widely used tools on argument segmentation and annotation tasks, namely, a rulebased PDTB full parser (Lin et al., 2010) , a BiLSTM-CRF model for discourse units segmentation (Wang et al., 2018) and an XLNet based discourse relations classifier (Yang et al., 2019) . Our experiments demonstrated that the PDTB parser achieved an F1 score of 22.61% on the span identification and 21.20% on the joint span identification and relation recognition tasks. This performance has been considered unsatisfactory for further use. Deep learning models, in contrast, showed significantly better performance: F1 scores ranging from 47.94% to 79.83% were achieved on the segmentation task, and accuracy of 60.22% and 50.48% for top-level and fine-grained discourse relation classification, respectively.",
                "cite_spans": [
                    {
                        "start": 214,
                        "end": 232,
                        "text": "(Lin et al., 2010)",
                        "ref_id": "BIBREF33"
                    },
                    {
                        "start": 287,
                        "end": 306,
                        "text": "(Wang et al., 2018)",
                        "ref_id": "BIBREF80"
                    },
                    {
                        "start": 357,
                        "end": 376,
                        "text": "(Yang et al., 2019)",
                        "ref_id": "BIBREF81"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Conclusions and Future Work",
                "sec_num": "5"
            },
            {
                "text": "We successfully applied the best performing models to segment and annotate the argumentative corpus Dagstuhl15512 ArgQuality and conducted the detailed error analysis. The obtained argumentative discourse units were manually corrected and annotated with the fine-grained PDTB discourse relations. This corpus contains 2,222 annotated unit pairs and presents a valuable resource for further argumentation mining studies and will be released to the community.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Conclusions and Future Work",
                "sec_num": "5"
            },
            {
                "text": "The obtained results opened up many interesting prospects for future research. For example, various argumentation schemes can be reconstructed based on the proposed approach, and evaluated within numerous contexts and domains. Argument and argumentation quality can be assessed and robust reasoning engines designed.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Conclusions and Future Work",
                "sec_num": "5"
            },
            {
                "text": "Stance is defined as an overall position held by a person towards an idea or attitude(Somasundaran and Wiebe, 2009).",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "Other competing hypotheses take an EDU to be a prosodic unit, a dialogue turn, a sentence, an intentionally defined discourse segment (e.g. utterance) or the contextually indexed representation of information.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "PDTB 2.0 is an extended version of the PDTB 1.0 corpus, where extensions concern annotations of implicit relations for the entire corpus, senses of all connectives and attribution of object type, scopal polarity and determinacy. Thus, for the purpose of this study, differences between PDTB 1.0 and PDTB 2.0 are not relevant.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "Here and in the following examples, a text span in the square brackets corresponds to an EDU obtained with a neural discourse segmenter; the manually corrected version is given after the arrow sign \u2212 \u2192.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            }
        ],
        "back_matter": [
            {
                "text": "The research reported in this paper was partly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project-ID 232722074 SFB 1102.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Acknowledgments",
                "sec_num": null
            }
        ],
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        "ref_entries": {
            "FIGREF0": {
                "uris": null,
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                "text": "Basic support relations and complex formations suggested byPeldszus and Stede (2013).",
                "type_str": "figure"
            },
            "FIGREF1": {
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                "text": "PDTB and RST-DT annotations for a WSJ 1172 paragraph(Demberg et al., 2019), where 1 refers to Arg1 and 2 to Arg2 in PDTB; N stands for Nucleus and S for Satellite in RST; and (a-d) are RST-DT's Elementary Discourse Units.Kuribayashi et al. (2018);Opitz and Frank (2019);Aker et al. (2017);Shnarch et al. (2018).",
                "type_str": "figure"
            },
            "FIGREF2": {
                "uris": null,
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                "text": "Figure 2compares PDTB and the RST Treebank (RST-DT,Carlson et al. (2003)) annotations of the WSJ-1172 paragraph of Penn Tree Bank(PTB, Marcus et al. (1993)).",
                "type_str": "figure"
            },
            "FIGREF3": {
                "uris": null,
                "num": null,
                "text": "Experimental workflow for the argument structure recognition.",
                "type_str": "figure"
            },
            "FIGREF4": {
                "uris": null,
                "num": null,
                "text": "Confusion matrix for L1 relation classification with the PDTB parser.",
                "type_str": "figure"
            },
            "FIGREF5": {
                "uris": null,
                "num": null,
                "text": "Confusion matrix for 5-class discourse relation classification task on Dagstuhl15512 ArgQuality.",
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            },
            "FIGREF6": {
                "uris": null,
                "num": null,
                "text": "Creationism tries to sneak the supernatural as a scientific explanation. Expansion.Restatement This is called pseudo -science. So a lousy father is better than none. Comparison.Concession (that is of course assuming that he is not abusive in any way) Books enlighten the soul. Expansion.Conjunction Books don't destroy the morals of children. and the big corporations like Dasani and Nestle would loose millions of dollars. Contingency.Cause It would hurt the economy severely .",
                "type_str": "figure"
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            "TABREF2": {
                "text": "",
                "content": "<table><tr><td>Performance (F1 scores) of the PDTB parser</td></tr><tr><td>developed by Lin et al. (2010) on various tasks. * eval-</td></tr><tr><td>uation performed on the section 23 of the PDTB 2.0</td></tr><tr><td>corpus; ** evaluation performed on the on full PDTB</td></tr><tr><td>2.0 corpus.</td></tr></table>",
                "num": null,
                "html": null,
                "type_str": "table"
            },
            "TABREF4": {
                "text": "Segmentation performance (F1 scores) with an overview of the exact and partial matches processed with NeuralEDUSeg(Wang et al., 2018) of the PDTB 2.0 corpus; and relation recognition accuracy for different classification scenarios applying the XLNet model(Yang et al., 2019) on PDTB 2.0 data.",
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