Argument mining is the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in natural language. Understanding argumentative structur...

/pdf/argument-mining-a-survey-3wuf60zrvp.pdf

Argument Mining: A Survey

Argument mining is a core technology for automating argument search in large document collections. Despite its usefulness for this task, most current approaches are designed for use only with specific text types and fall short when applied to heterogeneous texts. In this paper, we propose a new sentential annotation scheme that is reliably applicable by crowd workers to arbitrary Web texts. We source annotations for over 25,000 instances covering eight controversial topics. We show that integrating topic information into bidirectional long short-term memory networks outperforms vanilla BiLSTMs by more than 3 percentage points in F1 in two- and three-label cross-topic settings. We also show that these results can be further improved by leveraging additional data for topic relevance using multi-task learning.

/pdf/cross-topic-argument-mining-from-heterogeneous-sources-3ux64k0e0w.pdf

Cross-topic Argument Mining from Heterogeneous Sources

Argument mining is a core technology for enabling argument search in large corpora. However, most current approaches fall short when applied to heterogeneous texts. In this paper, we present an argument retrieval system capable of retrieving sentential arguments for any given controversial topic. By analyzing the highest-ranked results extracted from Web sources, we found that our system covers 89% of arguments found in expert-curated lists of arguments from an online debate portal, and also identifies additional valid arguments.

/pdf/argumentext-searching-for-arguments-in-heterogeneous-sources-3lcsecj13i.pdf

ArgumenText: Searching for Arguments in Heterogeneous Sources

One key consequence of the information revolution is a significant increase and a contamination of our information supply. The practice of fact checking won’t suffice to eliminate the biases in text data we observe, as the degree of factuality alone does not determine whether biases exist in the spectrum of opinions visible to us. To better understand controversial issues, one needs to view them from a diverse yet comprehensive set of perspectives. For example, there are many ways to respond to a claim such as “animals should have lawful rights”, and these responses form a spectrum of perspectives, each with a stance relative to this claim and, ideally, with evidence supporting it. Inherently, this is a natural language understanding task, and we propose to address it as such. Specifically, we propose the task of substantiated perspective discovery where, given a claim, a system is expected to discover a diverse set of well-corroborated perspectives that take a stance with respect to the claim. Each perspective should be substantiated by evidence paragraphs which summarize pertinent results and facts. We construct PERSPECTRUM, a dataset of claims, perspectives and evidence, making use of online debate websites to create the initial data collection, and augmenting it using search engines in order to expand and diversify our dataset. We use crowd-sourcing to filter out noise and ensure high-quality data. Our dataset contains 1k claims, accompanied with pools of 10k and 8k perspective sentences and evidence paragraphs, respectively. We provide a thorough analysis of the dataset to highlight key underlying language understanding challenges, and show that human baselines across multiple subtasks far outperform ma-chine baselines built upon state-of-the-art NLP techniques. This poses a challenge and opportunity for the NLP community to address.

Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims.

Given any argument on any controversial topic, how to counter it? This question implies the challenging retrieval task of finding the best counterargument. Since prior knowledge of a topic cannot be expected in general, we hypothesize the best counterargument to invoke the same aspects as the argument while having the opposite stance. To operationalize our hypothesis, we simultaneously model the similarity and dissimilarity of pairs of arguments, based on the words and embeddings of the arguments’ premises and conclusions. A salient property of our model is its independence from the topic at hand, i.e., it applies to arbitrary arguments. We evaluate different model variations on millions of argument pairs derived from the web portal idebate.org. Systematic ranking experiments suggest that our hypothesis is true for many arguments: For 7.6 candidates with opposing stance on average, we rank the best counterargument highest with 60% accuracy. Even among all 2801 test set pairs as candidates, we still find the best one about every third time.

/pdf/retrieval-of-the-best-counterargument-without-prior-topic-4wjxav6gxs.pdf

Retrieval of the Best Counterargument without Prior Topic Knowledge

Computational argumentation is expected to play a critical role in the future of web search. To make this happen, many search-related questions must be revisited, such as how people query for arguments, how to mine arguments from the web, or how to rank them. In this paper, we develop an argument search framework for studying these and further questions. The framework allows for the composition of approaches to acquiring, mining, assessing, indexing, querying, retrieving, ranking, and presenting arguments while relying on standard infrastructure and interfaces. Based on the framework, we build a prototype search engine, called args, that relies on an initial, freely accessible index of nearly 300k arguments crawled from reliable web resources. The framework and the argument search engine are intended as an environment for collaborative research on computational argumentation and its practical evaluation.

/pdf/building-an-argument-search-engine-for-the-web-4x0e2oy54r.pdf

Building an Argument Search Engine for the Web

Clickbait is increasingly used by publishers on social media platforms to spark their users’ natural curiosity and to elicit clicks on their content. Every click earns them display advertisement revenue. Social media users who are tricked into clicking may experience a sense of disappointment or agitation, and social media operators have been observing growing amounts of clickbait on their platforms. As largest video-sharing platform on the web, YouTube, too, suffers from clickbait. Many users and YouTubers alike have complained about this development. In this paper, we lay the foundation for crowdsourcing the first YouTube clickbait corpus by (1) augmenting the YouTube 8M dataset with meta data to obtain a large-scale base population of videos, and by (2) studying the task design suitable to manual clickbait identification.

/pdf/towards-crowdsourcing-clickbait-labels-for-youtube-videos-1zbk9dc38w.pdf

Jiani Qu

Papers

Building an Argument Search Engine for the Web

Towards Crowdsourcing Clickbait Labels for YouTube Videos.