A Corpus of Deep Argumentative Structures as an Explanation to Argumentative Relations
This work addresses the need for more interpretable and structured explanations in computational argumentation, though it is incremental as it builds on existing discourse analysis with a new annotation approach.
The paper tackles the problem of deep argumentative structure analysis by proposing a new task that explains argumentative relations using a small set of predefined patterns, achieving 74.6% coverage and 85.9% inter-annotator agreement on a test set.
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.