Aggregating Bipolar Opinions (With Appendix)
- URL: http://arxiv.org/abs/2102.02881v1
- Date: Thu, 4 Feb 2021 20:43:30 GMT
- Title: Aggregating Bipolar Opinions (With Appendix)
- Authors: Stefan Lauren and Francesco Belardinelli and Francesca Toni
- Abstract summary: We use Bipolar Assumption-based Argumentation (ABA) as an all-encompassing formalism for BA under different semantics.
We prove several preservation results, both positive and negative, for relevant properties of Bipolar ABA.
- Score: 19.899731557360223
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce a novel method to aggregate Bipolar Argumentation (BA)
Frameworks expressing opinions by different parties in debates. We use Bipolar
Assumption-based Argumentation (ABA) as an all-encompassing formalism for BA
under different semantics. By leveraging on recent results on judgement
aggregation in Social Choice Theory, we prove several preservation results,
both positive and negative, for relevant properties of Bipolar ABA.
Related papers
- Applying Attribution Explanations in Truth-Discovery Quantitative Bipolar Argumentation Frameworks [18.505289553533164]
Argument Attribution Explanations (AAEs) and Relation Attribution Explanations (RAEs) are used to explain the strength of arguments under gradual semantics.
We apply AAEs and RAEs to Truth Discovery QBAFs, which assess the trustworthiness of sources and their claims.
We find that both AAEs and RAEs can provide interesting explanations and can give non-trivial and surprising insights.
arXiv Detail & Related papers (2024-09-09T17:36:39Z) - CE-QArg: Counterfactual Explanations for Quantitative Bipolar Argumentation Frameworks (Technical Report) [18.505289553533164]
We propose an iterative algorithm named Counterfactual Explanations for Quantitative bipolar Argumentation frameworks (CE-QArg)
CE-QArg can identify valid and cost-effective counterfactual explanations based on two core modules, polarity and priority.
We discuss some formal properties of our counterfactual explanations and empirically evaluate CE-QArg on randomly generated QBAFs.
arXiv Detail & Related papers (2024-07-11T13:34:11Z) - Instantiations and Computational Aspects of Non-Flat Assumption-based Argumentation [18.32141673219938]
We study an instantiation-based approach for reasoning in possibly non-flat ABA.
We propose two algorithmic approaches for reasoning in possibly non-flat ABA.
arXiv Detail & Related papers (2024-04-17T14:36:47Z) - Polarity Calibration for Opinion Summarization [46.83053173308394]
Polarity calibration aims to align the polarity of output summary with that of input text.
We evaluate our model on two types of opinions summarization tasks: summarizing product reviews and political opinions articles.
arXiv Detail & Related papers (2024-04-02T07:43:12Z) - Towards Understanding Dual BN In Hybrid Adversarial Training [79.92394747290905]
We show that disentangling statistics plays a less role than disentangling affine parameters in model training.
We propose a two-task hypothesis which serves as the empirical foundation and a unified framework for Hybrid-AT improvement.
arXiv Detail & Related papers (2024-03-28T05:08:25Z) - Towards Distribution-Agnostic Generalized Category Discovery [51.52673017664908]
Data imbalance and open-ended distribution are intrinsic characteristics of the real visual world.
We propose a Self-Balanced Co-Advice contrastive framework (BaCon)
BaCon consists of a contrastive-learning branch and a pseudo-labeling branch, working collaboratively to provide interactive supervision to resolve the DA-GCD task.
arXiv Detail & Related papers (2023-10-02T17:39:58Z) - Non-flat ABA is an Instance of Bipolar Argumentation [23.655909692988637]
Assumption-based Argumentation (ABA) is a well-known structured argumentation formalism.
A common restriction imposed on ABA frameworks (ABAFs) is that they are flat.
No translation exists from general, possibly non-flat ABAFs into any kind of abstract argumentation formalism.
arXiv Detail & Related papers (2023-05-21T13:18:08Z) - PCL: Peer-Contrastive Learning with Diverse Augmentations for
Unsupervised Sentence Embeddings [69.87899694963251]
We propose a novel Peer-Contrastive Learning (PCL) with diverse augmentations.
PCL constructs diverse contrastive positives and negatives at the group level for unsupervised sentence embeddings.
PCL can perform peer-positive contrast as well as peer-network cooperation, which offers an inherent anti-bias ability.
arXiv Detail & Related papers (2022-01-28T13:02:41Z) - Collective Argumentation: The Case of Aggregating Support-Relations of
Bipolar Argumentation Frameworks [0.0]
We analyze what semantic properties of bipolar argumentation frameworks can be preserved by aggregation rules.
In this paper, we assume that under bipolar argumentation frameworks, individuals are equipped with a set of arguments and a set of attacks between arguments.
arXiv Detail & Related papers (2021-06-22T02:45:10Z) - Statistical Efficiency of Thompson Sampling for Combinatorial
Semi-Bandits [56.31950477139053]
We investigate multi-armed bandit with semi-bandit feedback (CMAB)
We analyze variants of the Combinatorial Thompson Sampling policy (CTS)
This last result gives us an alternative to the Efficient Sampling for Combinatorial Bandit policy (ESCB)
arXiv Detail & Related papers (2020-06-11T17:12:11Z) - M2P2: Multimodal Persuasion Prediction using Adaptive Fusion [65.04045695380333]
This paper solves two problems: the Debate Outcome Prediction (DOP) problem predicts who wins a debate and the Intensity of Persuasion Prediction (IPP) problem predicts the change in the number of votes before and after a speaker speaks.
Our M2P2 framework is the first to use multimodal (acoustic, visual, language) data to solve the IPP problem.
arXiv Detail & Related papers (2020-06-03T18:47:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.