·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Jack Dorsey is taking on Slack with Buzz, a group chat platform for teams and their AI agents26m◆AI and the rise of the universal entertainment app30m◆Substack adds an AI detector to help spot blogs written by no one47m◆Data centers expected to use 4x more electricity by 20352h◆Google releases three new Gemini models — but no 3.5 Pro2h◆Introducing the ChatGPT for small business program3h◆Anthropic’s $1.5 billion book piracy settlement approved by judge3h◆US threatens sanctions against Chinese AI models over IP theft4h◆Google launches a cheaper alternative to large AI security models like Mythos5h◆Music streamer Deezer says more than 50% of daily uploads are AI-generated6h◆Halliday’s latest smart glasses feature a much-improved display7h◆America needs to stop getting shocked by Chinese AI9h◆Advancing next-gen AI with materials science innovation9h◆Gritt exits stealth with $32 million for robots to build solar plants — then, everything else10h◆Capacity and Redundancy Trade-offs in Multi-Task Learning16h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation16h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making16h◆Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection16h◆Supervised Reward Inference16h◆PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization16h◆Jack Dorsey is taking on Slack with Buzz, a group chat platform for teams and their AI agents26m◆AI and the rise of the universal entertainment app30m◆Substack adds an AI detector to help spot blogs written by no one47m◆Data centers expected to use 4x more electricity by 20352h◆Google releases three new Gemini models — but no 3.5 Pro2h◆Introducing the ChatGPT for small business program3h◆Anthropic’s $1.5 billion book piracy settlement approved by judge3h◆US threatens sanctions against Chinese AI models over IP theft4h◆Google launches a cheaper alternative to large AI security models like Mythos5h◆Music streamer Deezer says more than 50% of daily uploads are AI-generated6h◆Halliday’s latest smart glasses feature a much-improved display7h◆America needs to stop getting shocked by Chinese AI9h◆Advancing next-gen AI with materials science innovation9h◆Gritt exits stealth with $32 million for robots to build solar plants — then, everything else10h◆Capacity and Redundancy Trade-offs in Multi-Task Learning16h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation16h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making16h◆Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection16h◆Supervised Reward Inference16h◆PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization16h◆
News/Beyond Clean Text: Evaluating Encoder and Decoder Robustness for Bangla Event Detection in Noisy Text
arxiv
PublishedJuly 1, 2026 at 4:00 AM
—neutral

Beyond Clean Text: Evaluating Encoder and Decoder Robustness for Bangla Event Detection in Noisy Text

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2606.30914v1 Announce Type: new Abstract: Event detection (ED) systems are typically evaluated on clean, curated text, leaving their robustness to real-world noise largely unexplored, particularly for low-resource languages such as Bangla. We introduce a generalized Bangla news event ontology

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivCapacity and Redundancy Trade-offs in Multi-Task Learning16harxivPredictive Training with Latent Imagination for Visual Quadruped Navigation16harxivWhere Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making16harxivDid We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection16h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews