Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userVerdict Inference with Claim and Retrieved Elements Using RoBERTa
استنتاج الحكم مع المطالبة والعناصر المستردة باستخدام روبرتا
332
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
اجتذبت التحقق من الحقائق التلقائي اهتماما بالبحوث الحديثة باعتباره نشر متزايد للتضليل على منصات وسائل التواصل الاجتماعي.تقدم المهمة المشتركة الحميرة معيارا للتحقق من الحقائق، حيث يتم تحدي النظام للتحقق من المطالبة المعينة باستخدام العناصر الواضحة المستخرجة من وثائق ويكيبيديا.في هذه الورقة، نقترح نظامنا الثالث الذي يتكون نظامنا الثالث من ثلاثة مراحل يتكون من استرجاع المستندات، واسترجاع العناصر، والاستدلال الحكم للمهمة المشتركة الحمية.من خلال النظر في أهمية السياق في مهمة استخراج الحقائق والتحقق، فإن نظامنا يحقق 0.29 درجة حمامة على مجموعة التطوير و 0.25 درجة حمامة على مجموعة الاختبار العمياء، سواء تتفوق على خط الأساس الحمير.
Automatic fact verification has attracted recent research attention as the increasing dissemination of disinformation on social media platforms. The FEVEROUS shared task introduces a benchmark for fact verification, in which a system is challenged to verify the given claim using the extracted evidential elements from Wikipedia documents. In this paper, we propose our 3rd place three-stage system consisting of document retrieval, element retrieval, and verdict inference for the FEVEROUS shared task. By considering the context relevance in the fact extraction and verification task, our system achieves 0.29 FEVEROUS score on the development set and 0.25 FEVEROUS score on the blind test set, both outperforming the FEVEROUS baseline.
References used
https://aclanthology.org/
rate research
Read More
Claim verification is challenging because it requires first to find textual evidence and then apply claim-evidence entailment to verify a claim. Previous works evaluate the entailment step based on the retrieved evidence, whereas we hypothesize that
the entailment prediction can provide useful signals for evidence retrieval, in the sense that if a sentence supports or refutes a claim, the sentence must be relevant. We propose a novel model that uses the entailment score to express the relevancy. Our experiments verify that leveraging entailment prediction improves ranking multiple pieces of evidence.
This article describes research on claim verification carried out using a multiple GAN-based model. The proposed model consists of three pairs of generators and discriminators. The generator and discriminator pairs are responsible for generating synt
hetic data for supported and refuted claims and claim labels. A theoretical discussion about the proposed model is provided to validate the equilibrium state of the model. The proposed model is applied to the FEVER dataset, and a pre-trained language model is used for the input text data. The synthetically generated data helps to gain information that improves classification performance over state of the art baselines. The respective F1 scores after applying the proposed method on FEVER 1.0 and FEVER 2.0 datasets are 0.65+-0.018 and 0.65+-0.051.
Given the current social distancing regulations across the world, social media has become the primary mode of communication for most people. This has isolated millions suffering from mental illnesses who are unable to receive assistance in person. Th
ey have increasingly turned to online platforms to express themselves and to look for guidance in dealing with their illnesses. Keeping this in mind, we propose a solution to classify mental illness posts on social media thereby enabling users to seek appropriate help. In this work, we classify five prominent kinds of mental illnesses- depression, anxiety, bipolar disorder, ADHD and PTSD by analyzing unstructured user data on Reddit. In addition, we share a new high-quality dataset1 to drive research on this topic. The dataset consists of the title and post texts from 17159 posts and 13 subreddits each associated with one of the five mental illnesses listed above or a None class indicating the absence of any mental illness. Our model is trained on Reddit data but is easily extensible to other social media platforms as well as demonstrated in our results.We believe that our work is the first multi-class model that uses a Transformer based architecture such as RoBERTa to analyze people's emotions and psychology. We also demonstrate how we stress test our model using behavioral testing. Our dataset is publicly available and we encourage researchers to utilize this to advance research in this arena. We hope that this work contributes to the public health system by automating some of the detection process and alerting relevant authorities about users that need immediate help.
Aspect-based Sentiment Analysis (ABSA), aiming at predicting the polarities for aspects, is a fine-grained task in the field of sentiment analysis. Previous work showed syntactic information, e.g. dependency trees, can effectively improve the ABSA pe
rformance. Recently, pre-trained models (PTMs) also have shown their effectiveness on ABSA. Therefore, the question naturally arises whether PTMs contain sufficient syntactic information for ABSA so that we can obtain a good ABSA model only based on PTMs. In this paper, we firstly compare the induced trees from PTMs and the dependency parsing trees on several popular models for the ABSA task, showing that the induced tree from fine-tuned RoBERTa (FT-RoBERTa) outperforms the parser-provided tree. The further analysis experiments reveal that the FT-RoBERTa Induced Tree is more sentiment-word-oriented and could benefit the ABSA task. The experiments also show that the pure RoBERTa-based model can outperform or approximate to the previous SOTA performances on six datasets across four languages since it implicitly incorporates the task-oriented syntactic information.
Certain types of classification problems may be performed at multiple levels of granularity; for example, we might want to know the sentiment polarity of a document or a sentence, or a phrase. Often, the prediction at a greater-context (e.g., sentenc
es or paragraphs) may be informative for a more localized prediction at a smaller semantic unit (e.g., words or phrases). However, directly inferring the most salient local features from the global prediction may overlook the semantics of this relationship. This work argues that inference along the contraposition relationship of the local prediction and the corresponding global prediction makes an inference framework that is more accurate and robust to noise. We show how this contraposition framework can be implemented as a transfer function that rewrites a greater-context from one class to another and demonstrate how an appropriate transfer function can be trained from a noisy user-generated corpus. The experimental results validate our insight that the proposed contrapositive framework outperforms the alternative approaches on resource-constrained problem domains.
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
