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Register a new userDanFEVER: claim verification dataset for Danish
Danfever: طلب بيانات التحقق من الدانماركية
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نقدم DataSet، Danfever، مخصص للبحث عن المعلومات الخاطئة متعددة اللغات.DataSet باللغة الدنماركية ولها نفس التنسيق مثل مجموعة بيانات الحمى الإنجليزية المعروفة.يمكن استخدامه للاختبار طرق في إعدادات متعددة اللغات، وكذلك لإنشاء الطرز في الإنتاج للغة الدنماركية.
We present a dataset, DanFEVER, intended for multilingual misinformation research. The dataset is in Danish and has the same format as the well-known English FEVER dataset. It can be used for testing methods in multilingual settings, as well as for creating models in production for the Danish language.
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This work describes the adaptation of a pretrained sequence-to-sequence model to the task of scientific claim verification in the biomedical domain. We propose a system called VerT5erini that exploits T5 for abstract retrieval, sentence selection, an
d label prediction, which are three critical sub-tasks of claim verification. We evaluate our pipeline on SciFACT, a newly curated dataset that requires models to not just predict the veracity of claims but also provide relevant sentences from a corpus of scientific literature that support the prediction. Empirically, our system outperforms a strong baseline in each of the three sub-tasks. We further show VerT5erini's ability to generalize to two new datasets of COVID-19 claims using evidence from the CORD-19 corpus.
We present an overview of the SCIVER shared task, presented at the 2nd Scholarly Document Processing (SDP) workshop at NAACL 2021. In this shared task, systems were provided a scientific claim and a corpus of research abstracts, and asked to identify
which articles Support or Refute the claim as well as provide evidentiary sentences justifying those labels. 11 teams made a total of 14 submissions to the shared task leaderboard, leading to an improvement of more than +23 F1 on the primary task evaluation metric. In addition to surveying the participating systems, we provide several insights into modeling approaches to support continued progress and future research on the important and challenging task of scientific claim verification.
Scientific claim verification can help the researchers to easily find the target scientific papers with the sentence evidence from a large corpus for the given claim. Some existing works propose pipeline models on the three tasks of abstract retrieva
l, rationale selection and stance prediction. Such works have the problems of error propagation among the modules in the pipeline and lack of sharing valuable information among modules. We thus propose an approach, named as ARSJoint, that jointly learns the modules for the three tasks with a machine reading comprehension framework by including claim information. In addition, we enhance the information exchanges and constraints among tasks by proposing a regularization term between the sentence attention scores of abstract retrieval and the estimated outputs of rational selection. The experimental results on the benchmark dataset SciFact show that our approach outperforms the existing works.
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.
This research suggests a new method that aims to verify the manual signature image which is written by person, and specify whether this signature back to this person or that forged signature. This was done by extracting geometric features of the sign
ature image and applying statistical functions on them as a way to verify the signature of that person.
The features from the signature image have been extracted on many stages so a signature image has been transformed from the gray scale to binary format, and then extracting the statistical features from the original signature image which is the maximum value from the most repeated values in the ones' coordination line that determine the signature shape, in addition to the number of ones which also determine the signature shape. Finally two ranges have been identified for the values accepted for original signature image. By the same way, statistical features have been extracted from the foreign signature image and tested if they aggregate within the specified domain of acceptable values. This research also includes the results of the proposed approach that compared with the previous methods in this scope. The proposed method has been tested to the data base consisting of 16200 signatures back to 300 persons, and as a result the signature image has been verified with a good percentage.
facial characteristic points-FCP
neuro_fuzzy controller
Pattern recognition
Signature image
Signature detection
Signature Image's features
off line Signature verification
statistical functions
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