Deep Argumentative Structure Analysis as an Explanation to Argumentative Relations

In recent years, there has been growing interest in the automatic analysis of argumentative texts [11], such as the identification of a shallow discourse structure for such texts by way of argumentative relation detection [1, 24, 13, etc.] and argumentative zoning [26, 10, 8, etc.]. Argumentative relation detection is the task of identifying argumentative relations (typically, support and attack relations) between discourse segments. Argumentative zoning is the task of identifying argumentative zones such as premise and claim. Suppose we are analyzing the following debate text discussing the topic “Should shopping malls be allowed to be open on holidays? ”: (1) [I as an employee find it practical to be able to shop on weekends. ]S1 [Sure, other people have to work in the shops on the weekend,]S2 [but they can have days off during the week]S3 In this text, segment S2 attacks segment S1 and segment S3 attacks, or undercuts, the relationship between S2 and S1 (argumentative relation detection). In another view, segments S2 and S3 serve as premises and segment S1 as a claim (argumentative zoning). The design of these shallow discourse analysis tasks has an advantage in their simplicity, which makes human annotation simple and reliable, achieving relatively high interannotator agreement [20]. Previous annotation studies thus have mainly focused on creating corpora for the identification of shallow discourse structures. In this work, we propose a task design for going beyond shallow discourse structure by analyzing argumentative texts at a deeper level. For this, we consider a task of explaining why it makes sense to interpret each support/attack relation. For instance, in Example 1, a reasonable explanation why S2 can be interpreted as an attack to S1 is the following: (2) (i) S1 states that (to) be able to shop on weekends (relevant to the topic) is a positive thing. (ii) S2 presupposes that (to) be able to shop on weekends will make other people to work in the shops on the weekends (consequence), and (iii) states that the consequence is an undesirable thing. (iv) Namely, S1 states a positive aspect of one thing whereas S2 states a negative consequence of the same thing; therefore, S2 attacks S1. We consider the task of producing such an explanation (i.e. the author’s logical reasoning) for each argumentative relation underlying a given argumentative text. This direction of task design has several advantages. First, understanding the logical reasoning behind an argumentative text contributes toward determining implicit argumentative relations not indicated with an explicit discourse marker. Analysis of implicit discourse relations is a long-standing open problem in discourse analysis [9, 2, etc.]. We expect that this direction of research will provide a new approach to it. Second, identifying the logical reasoning will be useful for a range of argumentation mining applications. One obvious example is to aggregate multiple arguments and produce a logic-based abstractive summary. It will also be required in automatically assessing the quality of the logic structure of a given argumentation (cf. automatic essay scoring [23, 27]). Furthermore, it will be useful for generation of rebuttals in application contexts where a human and machine are cooperatively engaged in a debate (for decision support or education). Shallow discourse structure analysis, as assumed in previous work, suffers a large gap between what it produces and what is required in these useful potential applications.