Taipei
- Asia > Middle East > Iran (0.34)
- Asia > Middle East > UAE (0.15)
- Asia > North Korea (0.14)
- (23 more...)
- Media > News (1.00)
- Government > Regional Government > North America Government > United States Government (1.00)
- Transportation > Freight & Logistics Services > Shipping (0.88)
- Government > Military > Navy (0.70)
- Information Technology > Communications > Social Media (0.73)
- Information Technology > Artificial Intelligence > Robots > Autonomous Vehicles > Drones (0.69)
- North America > United States > Pennsylvania > Philadelphia County > Philadelphia (0.04)
- Asia > Taiwan > Taiwan Province > Taipei (0.04)
- Asia > Japan > Honshū > Kantō > Tokyo Metropolis Prefecture > Tokyo (0.04)
- Information Technology > Artificial Intelligence > Vision (1.00)
- Information Technology > Artificial Intelligence > Natural Language (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.68)
- Asia > Taiwan > Taiwan Province > Taipei (0.04)
- South America > Colombia > Meta Department > Villavicencio (0.04)
- Research Report > New Finding (1.00)
- Research Report > Experimental Study (1.00)
- Europe > Spain > Andalusia > Granada Province > Granada (0.04)
- Europe > North Macedonia > Skopje Statistical Region > Skopje Municipality > Skopje (0.04)
- Europe > Italy > Apulia > Bari (0.04)
- (3 more...)
- Research Report > Experimental Study (1.00)
- Research Report > New Finding (0.67)
- North America > United States > Minnesota > Hennepin County > Minneapolis (0.14)
- Europe > Italy > Friuli Venezia Giulia > Trieste Province > Trieste (0.05)
- Europe > Italy > Tuscany > Florence (0.04)
- Asia > Taiwan > Taiwan Province > Taipei (0.04)
6d0f9c415e2d779c78f32b74668e9d02-Paper-Datasets_and_Benchmarks_Track.pdf
Fact-checking is extensively studied in the context of misinformation and disinformation, addressing objective inaccuracies. However, a softer form of misinformation involves responses that are factually correct but lack certain features such as clarity and relevance. This challenge is prevalent in formal Question-Answer (QA) settings such as press conferences in finance, politics, sports, and other domains, where subjective answers can obscure transparency. Despite this, there is a lack of manually annotated datasets for subjective features across multiple dimensions. To address this gap, we introduce SubjECTive-QA, a human annotated dataset on Earnings Call Transcripts' (ECTs) QA sessions as the answers given by company representatives are often open to subjective interpretations and scrutiny. The dataset includes 49, 446 annotations for long-form QA pairs across six features: Assertive, Cautious, Optimistic, Specific, Clear, and Relevant . These features are carefully selected to encompass the key attributes that reflect the tone of the answers provided during QA sessions across different domains. Our findings are that the best-performing Pre-trained Language Model (PLM), RoBERTa-base, has similar weighted F1 scores to Llama-3-70b-Chat on features with lower subjectivity, such as Relevant and Clear, with a mean difference of 2 .
- North America > United States > Georgia > Fulton County > Atlanta (0.05)
- Asia > India > Maharashtra > Mumbai (0.05)
- Asia > Middle East > UAE > Abu Dhabi Emirate > Abu Dhabi (0.04)
- (15 more...)
- Financial News (1.00)
- Research Report > New Finding (0.87)
- Media > News (1.00)
- Law (1.00)
- Banking & Finance > Trading (1.00)
- (3 more...)
- North America > United States > Minnesota > Hennepin County > Minneapolis (0.14)
- Europe > Germany > Bavaria > Upper Bavaria > Munich (0.05)
- North America > United States > Massachusetts > Suffolk County > Boston (0.04)
- (10 more...)
- North America > Canada (0.04)
- Europe > Belgium > Brussels-Capital Region > Brussels (0.04)
- North America > United States > California > San Diego County > San Diego (0.04)
- (5 more...)
- North America > United States > Maryland > Prince George's County > College Park (0.14)
- North America > United States > New Mexico > Los Alamos County > Los Alamos (0.04)
- North America > United States > Illinois > Cook County > Evanston (0.04)
- (4 more...)
- Oceania > Australia > New South Wales > Sydney (0.04)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
- Asia > Taiwan > Taiwan Province > Taipei (0.04)