Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userWhat to Fact-Check: Guiding Check-Worthy Information Detection in News Articles through Argumentative Discourse Structure
ما يجب التحقق منه: توجيه التحقق من التحقق من المعلومات جديرة بالتحقق في مقالات إخبارية من خلال هيكل خطاب جدلي
101
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تبدأ معظم الأساليب الموجودة لفحص الحقائق التلقائي مع قائمة مطالبات مسبقة للتحقق منها.نحن نبحث في المشكلة المفهمية لتحديد التصريحات في المقالات الإخبارية تستحق التحقق من الحقيقة.نبحث عن هيكل الوسيطة البالغ 95 مقالة إخبارية في مجال تغير المناخ الذي يتم فحصه من قبل علماء المناخ في ClimateFeedback.org.ونحن نفرج عن أول كائن مشروح متعدد الطبقات لكل من هيكل الخطاب الجدلي (أنواع الوسائط والعلاقات) وعلى البيانات التي تم فحصها في الواقع في المقالات الإخبارية.نناقش العلاقة بين هيكل الوسيطة وتستحق الاختيار البيانات وتطوير العديد من النماذج الأساسية للكشف عن البيانات التي تستحق الاختيار في مجال تغير المناخ.تظهر نتائجنا الأولية أن استخدام معلومات حول هيكل خطاب جدلي يظهر طفيفا ولكن تحسين إحصائيا على خط أساس من هيكل الخطاب المحلي.
Most existing methods for automatic fact-checking start with a precompiled list of claims to verify. We investigate the understudied problem of determining what statements in news articles are worthy to fact-check. We annotate the argument structure of 95 news articles in the climate change domain that are fact-checked by climate scientists at climatefeedback.org. We release the first multi-layer annotated corpus for both argumentative discourse structure (argument types and relations) and for fact-checked statements in news articles. We discuss the connection between argument structure and check-worthy statements and develop several baseline models for detecting check-worthy statements in the climate change domain. Our preliminary results show that using information about argumentative discourse structure shows slight but statistically significant improvement over a baseline of local discourse structure.
References used
https://aclanthology.org/
rate research
Read More
Pre-trained neural language models give high performance on natural language inference (NLI) tasks. But whether they actually understand the meaning of the processed sequences is still unclear. We propose a new diagnostics test suite which allows to
assess whether a dataset constitutes a good testbed for evaluating the models' meaning understanding capabilities. We specifically apply controlled corruption transformations to widely used benchmarks (MNLI and ANLI), which involve removing entire word classes and often lead to non-sensical sentence pairs. If model accuracy on the corrupted data remains high, then the dataset is likely to contain statistical biases and artefacts that guide prediction. Inversely, a large decrease in model accuracy indicates that the original dataset provides a proper challenge to the models' reasoning capabilities. Hence, our proposed controls can serve as a crash test for developing high quality data for NLI tasks.
Tables provide valuable knowledge that can be used to verify textual statements. While a number of works have considered table-based fact verification, direct alignments of tabular data with tokens in textual statements are rarely available. Moreover
, training a generalized fact verification model requires abundant labeled training data. In this paper, we propose a novel system to address these problems. Inspired by counterfactual causality, our system identifies token-level salience in the statement with probing-based salience estimation. Salience estimation allows enhanced learning of fact verification from two perspectives. From one perspective, our system conducts masked salient token prediction to enhance the model for alignment and reasoning between the table and the statement. From the other perspective, our system applies salience-aware data augmentation to generate a more diverse set of training instances by replacing non-salient terms. Experimental results on TabFact show the effective improvement by the proposed salience-aware learning techniques, leading to the new SOTA performance on the benchmark.
We present an actor-critic framework to induce subtopical structures in a news article for news discourse profiling. The model uses multiple critics that act according to known subtopic structures while the actor aims to outperform them. The content
structures constitute sentences that represent latent subtopic boundaries. Then, we introduce a hierarchical neural network that uses the identified subtopic boundary sentences to model multi-level interaction between sentences, subtopics, and the document. Experimental results and analyses on the NewsDiscourse corpus show that the actor model learns to effectively segment a document into subtopics and improves the performance of the hierarchical model on the news discourse profiling task.
The widespread use of the Internet and the rapid dissemination of information poses the challenge of identifying the veracity of its content. Stance detection, which is the task of predicting the position of a text in regard to a specific target (e.g
. claim or debate question), has been used to determine the veracity of information in tasks such as rumor classification and fake news detection. While most of the work and available datasets for stance detection address short texts snippets extracted from textual dialogues, social media platforms, or news headlines with a strong focus on the English language, there is a lack of resources targeting long texts in other languages. Our contribution in this paper is twofold. First, we present a German dataset of debate questions and news articles that is manually annotated for stance and emotion detection. Second, we leverage the dataset to tackle the supervised task of classifying the stance of a news article with regards to a debate question and provide baseline models as a reference for future work on stance detection in German news articles.
We present a generic method to compute thefactual accuracy of a generated data summarywith minimal user effort. We look at the prob-lem as a fact-checking task to verify the nu-merical claims in the text. The verification al-gorithm assumes that the
data used to generatethe text is available. In this paper, we describehow the proposed solution has been used toidentify incorrect claims about basketball tex-tual summaries in the context of the AccuracyShared Task at INLG 2021.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
