In this paper, we compose a new task for deep argumentative structure analysis that goes beyond shallow discourse structure analysis. The idea is that argumentative relations can reasonably be represented with a small set of predefined patterns. For example, using value judgment and bipolar causality, we can explain a support relation between two argumentative segments as follows: Segment 1 states that something is good, and Segment 2 states that it is good because it promotes something good when it happens. We are motivated by the following questions: (i) how do we formulate the task?, (ii) can a reasonable pattern set be created?, and (iii) do the patterns work? To examine the task feasibility, we conduct a three-stage, detailed annotation study using 357 argumentative relations from the argumentative microtext corpus, a small, but highly reliable corpus. We report the coverage of explanations captured by our patterns on a test set composed of 270 relations. Our coverage result of 74.6 indicates that argumentative relations can reasonably be explained by our small pattern set. Our agreement result of 85.9 shows that a reasonable inter-annotator agreement can be achieved. To assist with future work in computational argumentation, the annotated corpus is made publicly available.
Conventionally, discourse structure analysis has been studied in the context of discourse relation identification. Earlier work includes discourse theories such as rhetorical structure theory which aim at creating coherent, tree-like structures for describing texts, where text units are typically adjacent @cite_15 . Other theories such as cross-document structure theory focus on the identification of discourse relations spanned across multiple documents @cite_17 . The Penn Discourse TreeBank, the largest manually annotated corpus for discourse relations, targeted both implicit and explicit relation detection for either adjacent sentences or clauses @cite_4 . In the field of Argumentation mining, previous work has proposed several kinds of tasks such as structure identification task (e.g., support-attack relation detection) @cite_5 @cite_13 . In addition, a wide variety of corpora have been created in several domains including scientific articles, essays, and online discussions @cite_18 @cite_9 . These studies aim to capture the shallow structures of arguments and do not try to explain a writer's reasoning.
@cite_18 @cite_4 @cite_9 @cite_5 @cite_15 @cite_13 @cite_17
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Discourse relations bind smaller linguistic units into coherent texts. Automatically identifying discourse relations is difficult, because it requires understanding the semantics of the linked arguments. A more subtle challenge is that it is not enough to represent the meaning of each argument of a discourse relation, because the relation may depend on links between lower-level components, such as entity mentions. Our solution computes distributed meaning representations for each discourse argument by composition up the syntactic parse tree. We also perform a downward compositional pass to capture the meaning of coreferent entity mentions. Implicit discourse relations are then predicted from these two representations, obtaining substantial improvements on the Penn Discourse Treebank. We introduce a new approach to argumentation mining that we applied to a parallel German English corpus of short texts annotated with argumentation structure. We focus on structure prediction, which we break into a number of subtasks: relation identification, central claim identification, role classification, and function classification. Our new model jointly predicts different aspects of the structure by combining the different subtask predictions in the edge weights of an evidence graph; we then apply a standard MST decoding algorithm. This model not only outperforms two reasonable baselines and two datadriven models of global argument structure for the difficult subtask of relation identification, but also improves the results for central claim identification and function classification and it compares favorably to a complex mstparser pipeline. We provide a systematic study of previously proposed features for implicit discourse relation identification, identifying new feature combinations that optimize F1-score. The resulting classifiers achieve the best F1-scores to date for the four top-level discourse relation classes of the Penn Discourse Tree Bank: COMPARISON, CONTINGENCY, EXPANSION, and TEMPORAL. We further identify factors for feature extraction that can have a major impact on performance and determine that some features originally proposed for the task no longer provide performance gains in light of more powerful, recently discovered features. Our results constitute a new set of baselines for future studies of implicit discourse relation identification. Discourse structure is the hidden link between surface features and document-level properties, such as sentiment polarity. We show that the discourse analyses produced by Rhetorical Structure Theory (RST) parsers can improve document-level sentiment analysis, via composition of local information up the discourse tree. First, we show that reweighting discourse units according to their position in a dependency representation of the rhetorical structure can yield substantial improvements on lexicon-based sentiment analysis. Next, we present a recursive neural network over the RST structure, which offers significant improvements over classificationbased methods.