DeepAI AI Chat
Log In Sign Up

Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network

by   Martin Gleize, et al.

With the advancement in argument detection, we suggest to pay more attention to the challenging task of identifying the more convincing arguments. Machines capable of responding and interacting with humans in helpful ways have become ubiquitous. We now expect them to discuss with us the more delicate questions in our world, and they should do so armed with effective arguments. But what makes an argument more persuasive? What will convince you? In this paper, we present a new data set, IBM-EviConv, of pairs of evidence labeled for convincingness, designed to be more challenging than existing alternatives. We also propose a Siamese neural network architecture shown to outperform several baselines on both a prior convincingness data set and our own. Finally, we provide insights into our experimental results and the various kinds of argumentative value our method is capable of detecting.


Neural Argument Generation Augmented with Externally Retrieved Evidence

High quality arguments are essential elements for human reasoning and de...

Automatic Argument Quality Assessment -- New Datasets and Methods

We explore the task of automatic assessment of argument quality. To that...

Using Argument-based Features to Predict and Analyse Review Helpfulness

We study the helpful product reviews identification problem in this pape...

Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

Key point analysis is the task of extracting a set of concise and high-l...

A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis

Identifying the quality of free-text arguments has become an important t...

Towards a Holistic View on Argument Quality Prediction

Argumentation is one of society's foundational pillars, and, sparked by ...