Taiwan Province
Battlefield demand turning Taiwan into drone manufacturing hub
A standard pick-up truck is mounted with a launching system for eight Cobra-3120 loitering munitions. TAIPEI - After years of sourcing drones from a wide range of international suppliers, including China, Ukraine has a new entrant supporting its battlefield needs: Taiwan. The self-ruled island has quietly been ramping up exports of domestically produced drones to war-torn Ukraine, underscoring how its homegrown industry has advanced in recent years, evolving from a largely experimental sector into a burgeoning supplier of battlefield-relevant technology. The move, which also helps expand Taiwan's defense-industrial base, has seen the island sell well over 100,000 drones to Ukraine since last year alone, mainly via Poland and the Czech Republic, according to data provided by the Taipei-based Research Institute for Democracy, Society and Emerging Technology (DSET). In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.
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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 .
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