Argumentation for machine learning: a survey

Existing approaches using argumentation to aid or improve machine learning differ in the type of machine learning technique they consider, in their use of argumentation and in their choice of argumentation framework and semantics. This paper presents a survey of this relatively young field highlighting, in particular, its achievements to date, the applications it has been used for as well as the benefits brought about by the use of argumentation, with an eye towards its future

Mining bipolar argumentation frameworks from natural language text

We describe a methodology for mining topic-dependent Bipolar Argumentation Frameworks (BAFs) from natural language text. Our focus is on identifying attack and support argumentative re- lations between texts about the same topic, treating these texts as arguments when they are argumentatively related to other texts. We illustrate our methodology on a dataset of hotel reviews and outline some possible applications using the BAFs resulting from our methodology

Identifying attack and support argumentative relations using deep learning

We propose a deep learning architecture to capture argumentative relations of attack and support from one piece of text to an- other, of the kind that naturally occur in a debate. The architecture uses two (uni- directional or bidirectional) Long Short- Term Memory networks and (trained or non-trained) word embeddings, and al- lows to considerably improve upon exist- ing techniques that use syntactic features and supervised classifiers for the same form of (relation-based) argument mining

Argumentation-based recommendations: fantastic explanations and how to find them

A significant problem of recommender systems is their inability to explain recommendations, resulting in turn in ineffective feedback from users and the inability to adapt to users’ preferences. We propose a hybrid method for calculating predicted ratings, built upon an item/aspect-based graph with users’ partially given ratings, that can be naturally used to provide explanations for recommendations, extracted from user-tailored Tripolar Argumentation Frameworks (TFs). We show that our method can be understood as a gradual semantics for TFs, exhibiting a desirable, albeit weak, property of balance. We also show experimentally that our method is competitive in generating correct predictions, compared with state-of-the-art methods, and illustrate how users can interact with the generated explanations to improve quality of recommendations

A platform for crowdsourcing corpora for argumentative

One problem that Argument Mining (AM) is facing is the difficultyof obtaining suitable annotated corpora. We propose a web-basedplatform, BookSafari, that allows crowdsourcing of annotated cor-pora forrelation-based AMfrom users providing reviews for booksand exchanging opinions about these reviews to facilitate argumen-tative dialogue. The annotations amount to pairwise argumentativerelations ofattackandsupportbetween opinions and between opin-ions and reviews. As a result of the annotations, reviews and opinionsform structured debates which can be understood as bipolar argu-mentation frameworks. The platform also empowers annotationsof the same pairs by multiple annotators and can support differentmeasures of inter-annotator agreement and corpora selection

A dataset independent set of baselines for relation prediction in argument mining.

Argument Mining is the research area which aims at extracting argument components and predicting argumentative relations (i.e.,support and attack) from text. In particular, numerous approaches have been proposed in the literature to predict the relations holding between the arguments, and application-specific annotated resources were built for this purpose. Despite the fact that these resources have been created to experiment on the same task, the definition of a single relation prediction method to be successfully applied to a significant portion of these datasets is an open research problem in Argument Mining. This means that none of the methods proposed in the literature can be easily ported from one resource to another. In this paper, we address this problem by proposing a set of dataset independent strong neural baselines which obtain homogeneous results on all the datasets proposed in the literature for the argumentative relation prediction task. Thus, our baselines can be employed by the Argument Mining community to compare more effectively how well a method performs on the argumentative relation prediction task

Combining deep learning and argumentative reasoning for the analysis of social media textual content using small datasets

The use of social media has become a regular habit for many and has changed the way people interact with each other. In this article, we focus on analysing whether news headlines support tweets and whether reviews are deceptive by analysing the interaction or the influence that these texts have on the others, thus exploiting contextual information. Concretely, we define a deep learning method for Relation-based Argument Mining to extract argumentative relations of attack and support. We then use this method for determining whether news articles support tweets, a useful task in fact-checking settings, where determining agreement towards a statement is a useful step towards determining its truthfulness. Furthermore we use our method for extracting Bipolar Argumentation Frameworks from reviews to help detect whether they are deceptive. We show experimentally that our method performs well in both settings. In particular, in the case of deception detection, our method contributes a novel argumentative feature that, when used in combination with other features in standard supervised classifiers, outperforms the latter even on small datasets

Explanatory predictions with artificial neural networks and argumentation

Data-centric AI has proven successful in several domains, but its outputs are often hard to explain. We present an architecture combining Artificial Neural Networks (ANNs) for feature selection and an instance of Abstract Argumentation (AA) for reasoning to provide effective predictions, explain- able both dialectically and logically. In particular, we train an autoencoder to rank features in input ex- amples, and select highest-ranked features to gen- erate an AA framework that can be used for mak- ing and explaining predictions as well as mapped onto logical rules, which can equivalently be used for making predictions and for explaining. We show empirically that our method significantly out- performs ANNs and a decision-tree-based method from which logical rules can also be extracted

A System for Supporting the Detection of Deceptive Reviews Using Argument Mining

The unstoppable rise of social networks and the web is facing a serious challenge: identifying the truthfulness of online opinions and reviews. We propose a system to identify two new argumentative features that a trained classifier can use to help determine whether a review is deceptive

Data-empowered argumentation for dialectically explainable predictions

Today’s AI landscape is permeated by plentiful data anddominated by powerful data-centric methods with the potential toimpact a wide range of human sectors. Yet, in some settings this po-tential is hindered by these data-centric AI methods being mostlyopaque. Considerable efforts are currently being devoted to defin-ing methods for explaining black-box techniques in some settings,while the use of transparent methods is being advocated in others,especially when high-stake decisions are involved, as in healthcareand the practice of law. In this paper we advocate a novel transpar-ent paradigm of Data-Empowered Argumentation (DEAr in short)for dialectically explainable predictions. DEAr relies upon the ex-traction of argumentation debates from data, so that the dialecticaloutcomes of these debates amount to predictions (e.g. classifications)that can be explained dialectically. The argumentation debates con-sist of (data) arguments which may not be linguistic in general butmay nonetheless be deemed to be ‘arguments’ in that they are dialec-tically related, for instance by disagreeing on data labels. We illus-trate and experiment with the DEAr paradigm in three settings, mak-ing use, respectively, of categorical data, (annotated) images and text.We show empirically that DEAr is competitive with another transpar-ent model, namely decision trees (DTs), while also providing natu-rally dialectical explanations