Stance detection with knowledge enhanced bert
Webb1 jan. 2024 · In this paper, we propose a novel BERT-based fine-tuning method that enhances the masked language model for stance detection. Instead of random token masking, we propose using a weighted log-odds ... WebbKnowledge Enhanced Target-Aware Stance Detection on Tweets Xin Zhang 1,JianhuaYuan, Yanyan Zhao , and Bing Qin1,2(B) ... • We provide a simple way to fuse knowledge from multiple external knowledge bases and outperform the BERT model by a large margin, especially on cases where targets are not explicitly mentioned in texts. 2 Related Work
Stance detection with knowledge enhanced bert
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Webb77 papers with code • 6 benchmarks • 21 datasets. Stance detection is the extraction of a subject's reaction to a claim made by a primary actor. It is a core part of a set of approaches to fake news assessment. Example: Source: "Apples are the most delicious fruit in existence". Reply: "Obviously not, because that is a reuben from Katz's". Webb13 apr. 2024 · Rumors may bring a negative impact on social life, and compared with pure textual rumors, online rumors with multiple modalities at the same time are more likely to mislead users and spread, so multimodal rumor detection cannot be ignored. Current detection methods for multimodal rumors do not focus on the fusion of text and picture …
Webb16 feb. 2024 · 通过以上过程，我们过滤掉了知识库D中不相关的知识 “Meanwhile, previous work [8] has shown that extracting consistent and inconsistent relations from commonsense knowledge is a promising approach to enhancing stance detection performance, therefore we unified the relations in ConceptNet and WikiData, and … Webb6 okt. 2024 · Therefore, in this paper, we propose two claim stance detection models, one is text-transformers based on efficient ensemble learning method, and the other is …
WebbFine-tuned language models using large-scale in-domain data have been shown to be the new state-of-the-art for many NLP tasks, including stance detection. In this paper, we propose a novel BERT-based fine-tuning method that enhances the masked language model for stance detection. Webb7 juli 2024 · Different from conventional stance detection, Zero-Shot Stance Detection (ZSSD) needs to predict the stances of the unseen targets during the inference stage. For human beings, we generally tend to reason the stance of a new target by linking it with the related knowledge learned from the known ones.
Webb21 maj 2024 · Download a PDF of the paper titled Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media, by Hema Karande and 3 other …
WebbThis paper designs target-aware prompts and proposes a novel verbalizer that distill the information learned from multiple prompts in stance detection, inspired by the potential … hauke-haien-koogWebb5. Google’s BERT: Bidirectional Encoder Representations (BERT) considers both the left and right sides of a word to determine its context. BERT is capable of multitask-learning and, performing different NLP tasks simultaneously. BERT is the first bidirectional and deep system for unsupervised learning of NLP tasks. hauke von sethWebbThis is challenging when the model lacks background knowledge about the target. Here, we show how background knowledge from Wikipedia can help enhance the performance on stance detection. We introduce Wikipedia Stance Detection BERT (WS-BERT) that infuses the knowledge into stance encoding. Extensive results on three benchmark datasets … haukeliWebbStance detection is a basic study of text opinion mining, which usually has two key inputs: (1) a target and (2) a post or comment made by an author. Given two inputs, the purpose of stance detection is to analyze the stance tendency such as “favor, against or neutral” towards specific targets expressed in the text. haukeliseter turisthytteWebb6 okt. 2024 · Therefore, in this paper, we propose two claim stance detection models, one is text-transformers based on efficient ensemble learning method, and the other is through knowledge-enhanced text-transformers model without introducing additional data. The rest of the paper is organized as follows: Sect. 2 introduces the dataset of this task. haukeliseterWebbTable 1. The examples of stance classification task. Given a tweet and the involved topic, the stance classifier is capable of detecting the stance label automatically. Example 1: Don’t be selfish. Stay home, reduce the spread, and safe lives. If you have to go out, please wear a mask and gloves. Topic: Stay at Home Orders Stance label: Favor haukemiaWebb8 apr. 2024 · Stance detection infers a text author's attitude towards a target. This is challenging when the model lacks background knowledge about the target. Here, we show how background knowledge from Wikipedia can help enhance the … hauken