In computational argumentation, so-called quality dimensions such as coherence or rhetoric are often used for ranking arguments. However, the literature often only predicts which argument is more persuasive, but not why this is the case. In this paper, we introduce AQUAPLANE, a transparent and easy-to-extend application that not only decides for a pair of arguments which one is more convincing with respect to a statement, but also provides an explanation.

Computational argumentation and especially argument mining together with retrieval enjoys increasing popularity. In contrast to standard search engines that focus on finding documents relevant to a query, argument retrieval aims at finding the best supporting and attacking premises given a query claim, e.g., from a predefined collection of arguments. Here, a claim is the central part of an argument representing the standpoint of a speaker with the goal to persuade the audience, and a premise serves as evidence to the claim. In addition to the actual retrieval process, existing work has focused on (1) classifying polarities of arguments into supporting or opposing, (2) classifying arguments by their frames (such as economic or environmental), and (3) clustering similar arguments by their meaning to avoid repetitions in the result list. For experiments, either hand-made argument collections or arguments extracted from debate portals were used. In this paper, we extend existing work on argument clustering, making the following contributions: First, we introduce a novel pipeline for clustering arguments. While previous work classified arguments either by polarity, frame, or meaning, our pipeline incorporates these three, allowing a more systematic presentation of arguments. Second, we introduce a new dataset consisting of 365 argument graphs accompanying more than 11,000 high-quality arguments that, contrary to previous datasets, have been generated, displayed, and verified by journalists and were published in newspapers. A thorough evaluation with this dataset provides a first baseline for future work.

With the Web augmenting every day and computers increasingly getting more powerful, research in the field of computational argumentation becomes more and more important. One of its research branches is argument retrieval, which aims at finding and presenting users the best arguments for their queries. Several systems already exist for this purpose, all having the same goal but reaching it in different ways. In line with existing work, an argument consists of a claim supported or attacked by a premise. Now that argument retrieval has become a separate task in the CLEF lab Touché, displaying the ranking is becoming increasingly important.

Argument search engines identify, extract, and rank the most important arguments for and against a given controversial topic. A number of such systems have recently been developed, usually focusing on classic information retrieval ranking methods that are based on frequency information. An important aspect that has been ignored so far by search engines is the quality of arguments. We present a quality-aware ranking framework for arguments already extracted from texts and represented as argument graphs, considering multiple established quality measures. An extensive evaluation with a standard benchmark collection demonstrates that taking quality into account significantly helps to improve retrieval quality for argument search. We also publish a dataset in which arguments with respect to topics were tediously annotated by humans with three widely accepted argument quality dimensions.

This paper targets the automated extraction of components of argumentative information and their relations from natural language text.Moreover, we address a current lack of systems to provide complete argumentative structure from arbitrary natural language text for general usage.We present an argument mining pipeline as a universally applicable approach for transforming German and English language texts to graph-based argument representations.We also introduce new methods for evaluating the results based on existing benchmark argument structures.Our results show that the generated argument graphs can be beneficial to detect new connections between different statements of an argumentative text.Our pipeline implementation is publicly available on GitHub.

Research on computational argumentation has recently become very popular. An argument consists of a claim that is supported or attacked by at least one premise. Its intention is the persuasion of others. An important problem in this field is retrieving good premises for a designated claim from a corpus of arguments. Given a claim, oftentimes existing approaches' first step is finding textually similar claims. In this paper we compare 196 methods systematically for determining similar claims by textual similarity, using a large corpus of (claim, premise) pairs crawled from debate portals. We also evaluate how well textual similarity of claims can predict relevance of the associated premises.

Stack Overflow (SO) is the most popular question-and-answer website for software developers, providing a large amount of code snippets and free-form text on a wide variety of topics. Like other software artifacts, questions and answers on SO evolve over time, for example when bugs in code snippets are fixed, code is updated to work with a more recent library version, or text surrounding a code snippet is edited for clarity. To be able to analyze how content on SO evolves, we built SOTorrent, an open dataset based on the official SO data dump. SOTorrent provides access to the version history of SO content at the level of whole posts and individual text or code blocks. It connects SO posts to other platforms by aggregating URLs from text blocks and by collecting references from GitHub files to SO posts. In this paper, we describe how we built SOTorrent, and in particular how we evaluated 134 different string similarity metrics regarding their applicability for reconstructing the version history of text and code blocks. Based on a first analysis using the dataset, we present insights into the evolution of SO posts, e.g., that post edits are usually small, happen soon after the initial creation of the post, and that code is rarely changed without also updating the surrounding text. Further, our analysis revealed a close relationship between post edits and comments. Our vision is that researchers will use SOTorrent to investigate and understand the evolution of SO posts and their relation to other platforms such as GitHub.