Leveraging Commonsense Knowledge on Classifying False News and Determining Checkworthiness of Claims

• URL: http://arxiv.org/abs/2108.03731v1
• Date: Sun, 8 Aug 2021 20:52:45 GMT
• Title: Leveraging Commonsense Knowledge on Classifying False News and Determining Checkworthiness of Claims
• Authors: Ipek Baris Schlicht, Erhan Sezerer, Selma Tekir, Oul Han, Zeyd Boukhers
• Abstract summary: We propose to leverage commonsense knowledge for the tasks of false news classification and check-worthy claim detection. We fine-tune the BERT language model with a commonsense question answering task and the aforementioned tasks in a multi-task learning environment. Our experimental analysis demonstrates that commonsense knowledge can improve performance in both tasks.
• Score: 1.487444917213389
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Widespread and rapid dissemination of false news has made fact-checking an indispensable requirement. Given its time-consuming and labor-intensive nature, the task calls for an automated support to meet the demand. In this paper, we propose to leverage commonsense knowledge for the tasks of false news classification and check-worthy claim detection. Arguing that commonsense knowledge is a factor in human believability, we fine-tune the BERT language model with a commonsense question answering task and the aforementioned tasks in a multi-task learning environment. For predicting fine-grained false news types, we compare the proposed fine-tuned model's performance with the false news classification models on a public dataset as well as a newly collected dataset. We compare the model's performance with the single-task BERT model and a state-of-the-art check-worthy claim detection tool to evaluate the check-worthy claim detection. Our experimental analysis demonstrates that commonsense knowledge can improve performance in both tasks.

Related papers

• How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models [95.44559524735308]
  Large language or multimodal model based verification has been proposed to scale up online policing mechanisms for mitigating spread of false and harmful content. We test the limits of improving foundation model performance without continual updating through an initial study of knowledge transfer. Our results on two recent multi-modal fact-checking benchmarks, Mocheg and Fakeddit, indicate that knowledge transfer strategies can improve Fakeddit performance over the state-of-the-art by up to 1.7% and Mocheg performance by up to 2.9%.
  arXiv  Detail & Related papers  (2024-06-29T08:39:07Z)
• Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction [50.62245481416744]
  We present the first benchmark that simulates the evaluation of open information extraction models in the real world. We design and annotate a large-scale testbed in which each example is a knowledge-invariant clique. By further elaborating the robustness metric, a model is judged to be robust if its performance is consistently accurate on the overall cliques.
  arXiv  Detail & Related papers  (2023-05-23T12:05:09Z)
• Fairness-guided Few-shot Prompting for Large Language Models [93.05624064699965]
  In-context learning can suffer from high instability due to variations in training examples, example order, and prompt formats. We introduce a metric to evaluate the predictive bias of a fixed prompt against labels or a given attributes. We propose a novel search strategy based on the greedy search to identify the near-optimal prompt for improving the performance of in-context learning.
  arXiv  Detail & Related papers  (2023-03-23T12:28:25Z)
• A practical method for occupational skills detection in Vietnamese job listings [0.16114012813668932]
  Lack of accurate and timely labor market information leads to skill miss-matches. Traditional approaches rely on existing taxonomy and/or large annotated data. We propose a practical methodology for skill detection in Vietnamese job listings.
  arXiv  Detail & Related papers  (2022-10-26T10:23:18Z)
• An Adversarial Benchmark for Fake News Detection Models [0.065268245109828]
  We formulate adversarial attacks that target three aspects of "understanding" We test our benchmark using BERT classifiers fine-tuned on the LIAR arXiv:arch-ive/1705648 and Kaggle Fake-News datasets.
  arXiv  Detail & Related papers  (2022-01-03T23:51:55Z)
• Hidden Biases in Unreliable News Detection Datasets [60.71991809782698]
  We show that selection bias during data collection leads to undesired artifacts in the datasets. We observed a significant drop (>10%) in accuracy for all models tested in a clean split with no train/test source overlap. We suggest future dataset creation include a simple model as a difficulty/bias probe and future model development use a clean non-overlapping site and date split.
  arXiv  Detail & Related papers  (2021-04-20T17:16:41Z)
• A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification [15.121389624346927]
  It is a non-trivial task to verify a scientific claim by providing supporting or rationale evidences. In this work, we propose a paragraph-level, multi-task learning model for the SciFact task by directly computing a sequence of contextualized sentence embeddings from a BERT model and jointly training the model on rationale selection and stance prediction.
  arXiv  Detail & Related papers  (2020-12-28T21:51:31Z)
• Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question Answering [80.60605604261416]
  We propose a novel neuro-symbolic framework for zero-shot question answering across commonsense tasks. We vary the set of language models, training regimes, knowledge sources, and data generation strategies, and measure their impact across tasks. We show that, while an individual knowledge graph is better suited for specific tasks, a global knowledge graph brings consistent gains across different tasks.
  arXiv  Detail & Related papers  (2020-11-07T22:52:21Z)
• On Commonsense Cues in BERT for Solving Commonsense Tasks [22.57431778325224]
  BERT has been used for solving commonsense tasks such as CommonsenseQA. We quantitatively investigate the presence of structural commonsense cues in BERT when solving commonsense tasks, and the importance of such cues for the model prediction.
  arXiv  Detail & Related papers  (2020-08-10T08:12:34Z)
• Generating Fact Checking Explanations [52.879658637466605]
  A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process. This paper provides the first study of how these explanations can be generated automatically based on available claim context. Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system.
  arXiv  Detail & Related papers  (2020-04-13T05:23:25Z)

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.