Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAdversarial Training for News Stance Detection: Leveraging Signals from a Multi-Genre Corpus.
التدريب الخصم للكشف عن موقف الأخبار: الاستفادة من إشارات من كوربوس متعدد الأنواع.
367
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
يشكل التعميم الشامل مسألة مهمة للكشف عن الموقف (SD).في هذه الورقة القصيرة، نقوم بالتحقيق في SD الصلبة العدسة، حيث يتم الاستفادة من المعرفة من البيانات التي تم إنشاؤها من قبل المستخدم لتحسين الأخبار SD على أهداف غير مرئية أثناء التدريب.نقوم بتنفيذ شبكة مخدرة قائمة على Bert وتظهر تحسينات الأداء التجريبية على مجموعة من خطوط الأساس القوية.بالنظر إلى وفرة البيانات التي تم إنشاؤها من قبل المستخدم، والتي تكون أقل تكلفة بكثير لاسترداد وتعليقها من المقالات الإخبارية، فإن هذا يشكل اتجاها بحثا واعدا.
Cross-target generalization constitutes an important issue for news Stance Detection (SD). In this short paper, we investigate adversarial cross-genre SD, where knowledge from annotated user-generated data is leveraged to improve news SD on targets unseen during training. We implement a BERT-based adversarial network and show experimental performance improvements over a set of strong baselines. Given the abundance of user-generated data, which are considerably less expensive to retrieve and annotate than news articles, this constitutes a promising research direction.
References used
https://aclanthology.org/
rate research
Read More
Stance detection on social media can help to identify and understand slanted news or commentary in everyday life. In this work, we propose a new model for zero-shot stance detection on Twitter that uses adversarial learning to generalize across topic
s. Our model achieves state-of-the-art performance on a number of unseen test topics with minimal computational costs. In addition, we extend zero-shot stance detection to topics not previously considered, highlighting future directions for zero-shot transfer.
Advertising on e-commerce and social media sites deliver ad impressions at web scale on a daily basis driving value to both shoppers and advertisers. This scale necessitates programmatic ways of detecting unsuitable content in ads to safeguard custom
er experience and trust. This paper focusses on techniques for training text classification models under resource constraints, built as part of automated solutions for advertising content moderation. We show how weak supervision, curriculum learning and multi-lingual training can be applied effectively to fine-tune BERT and its variants for text classification tasks in conjunction with different data augmentation strategies. Our extensive experiments on multiple languages show that these techniques detect adversarial ad categories with a substantial gain in precision at high recall threshold over the baseline.
Sarcasm detection is one of the top challenging tasks in text classification, particularly for informal Arabic with high syntactic and semantic ambiguity. We propose two systems that harness knowledge from multiple tasks to improve the performance of
the classifier. This paper presents the systems used in our participation to the two sub-tasks of the Sixth Arabic Natural Language Processing Workshop (WANLP); Sarcasm Detection and Sentiment Analysis. Our methodology is driven by the hypothesis that tweets with negative sentiment and tweets with sarcasm content are more likely to have offensive content, thus, fine-tuning the classification model using large corpus of offensive language, supports the learning process of the model to effectively detect sentiment and sarcasm contents. Results demonstrate the effectiveness of our approach for sarcasm detection task over sentiment analysis 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.
Stance detection (SD) entails classifying the sentiment of a text towards a given target, and is a relevant sub-task for opinion mining and social media analysis. Recent works have explored knowledge infusion supplementing the linguistic competence a
nd latent knowledge of large pre-trained language models with structured knowledge graphs (KGs), yet few works have applied such methods to the SD task. In this work, we first perform stance-relevant knowledge probing on Transformers-based pre-trained models in a zero-shot setting, showing these models' latent real-world knowledge about SD targets and their sensitivity to context. We then train and evaluate new knowledge-enriched stance detection models on two Twitter stance datasets, achieving state-of-the-art performance on both.
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
